Name: Clustering and Classification with Machine Learning in Python
Price: 135.99 GBP
Availability: InStock

Packt

Python Scripting

Automation

Get up to speed with automating repetitive tasks with Python

Complete Python Scripting for Automation

General Admission

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Did you know that Python is one of the most popular programming languages in the world, with a massive 8.2 million users? It&#39;s no wonder the demand for skilled Python programmers is increasing daily. If you&#39;re looking to learn Python and launch a career as a programmer, look no further than our <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Programmer Complete Bundle - QLS Endorsed.</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Our bundle includes 10 comprehensive courses designed to give learners the aptitudes and understanding they need to become successful Python programmers. From Python programming for non-programmers to data science, machine learning, and web development, our courses cover everything you need to know to become a skilled Python programmer.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Why do people in the UK learn Python programming? Here&#39;s a startling statistic: the demand for Python programming skills has increased by 22.5% in the UK in the last year alone, with an average salary of &pound;45,000 per annum. By taking our Python Programmer Complete Bundle, learners can gain the skills and knowledge they need to take advantage of this growing demand and launch a successful career in programming.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">And the best part? Our bundle is endorsed by the Quality Licence Scheme, ensuring the highest quality of education. Upon completion of the courses, learners will receive <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">10 QLS Endorsed Certificates in Hard Copy, free of charge.</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Take your first step towards a successful career in programming today with our <strong id="isPasted" fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Programmer Complete Bundle - QLS Endorsed</strong> and become a proficient Python programmer in no time.<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></strong></p><p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This bundle comprises the following courses:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">QLS Endorsed Courses:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 01:</strong> Python Programming for Non-Programmers Level 5</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 02:</strong> Python Data Science for Beginners</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 03:</strong> Machine Learning with Python</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 04:</strong> Azure Machine Learning</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 05:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 06:</strong> Introduction to Java</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 07:</strong> HTML Web Development Crash Course</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 08:</strong> How To Build Easy Drag &amp; Drop Landing Pages With WordPress</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 09:</strong> Career Coaching</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 10:</strong> The Unstoppable Self-Confidence &amp; High Self-Esteem Blueprint</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Outcomes:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Understand the fundamentals of Python programming</li><li fr-original-style="" style="box-sizing: border-box;">Use Python for data science and analysis</li><li fr-original-style="" style="box-sizing: border-box;">Apply machine learning techniques using Python</li><li fr-original-style="" style="box-sizing: border-box;">Understand the basics of Azure Machine Learning</li><li fr-original-style="" style="box-sizing: border-box;">Understand the basics of statistics and probability for data science and machine learning</li><li fr-original-style="" style="box-sizing: border-box;">Create web pages using HTML and WordPress</li><li fr-original-style="" style="box-sizing: border-box;">Develop the aptitudes and confidence needed to pursue a successful career in programming</li></ul><p dir="ltr" id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Why Choose Imperial Academy?</strong></span></p><ul fr-original-style="" style="box-sizing: border-box;"><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">Fully Online Training</span></li><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">QLS Endorsed &amp; CPD Accredited Certificate</span></li><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">No Time Limits. Study at Your Own Pace</span></li><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">Expert Instructors</span></li><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">Audio Visual Training</span></li><li dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(15, 15, 15); box-sizing: border-box;" fr-original-style="color:#0f0f0f;">Up-to-date Curriculum and Valuable Guidelines</span></li></ul><h2 style="color: rgb(65, 65, 65); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(65, 65, 65);">Course&nbsp;Description</h2><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Our <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Programmer Complete Bundle - QLS Endorsed</strong> is a comprehensive package of ten courses designed to provide learners with the knowledge needed to become successful Python programmers. Our courses cover a wide range of topics, including Python programming for non-programmers, data science, machine learning, and web development.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Whether you&#39;re an aspiring programmer or a business owner looking to develop your website, our Python Programmer Complete Bundle has something for you. By completing our courses, learners will gain the skills and confidence needed to pursue a successful career in programming and data analysis. With our <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">QLS Endorsed Hard Copy Certificates,</strong> learners can be confident in the quality of their education and prepared to take advantage of the growing demand for Python programming skills.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Certification:&nbsp;</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The Quality Licence Scheme ensures excellence and quality. These courses&#39; learning outcomes also provide the learners with high competence in their line of work. So,<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> QLS Endorsed</strong> courses deliver deep knowledge and add significant value to the resume.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">After completing the assessments, learners will be eligible for a prestigious <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">QLS Certificate</strong> for each endorsed course in this bundle. Moreover, the certificates will be delivered to their home free of cost!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Imperial Academy is an approved reselling partner for Quality Licence Scheme courses under One Education.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Who is this course for?</div>
    </h2><div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Individuals interested in a career in programming and data analysis</li><li fr-original-style="" style="box-sizing: border-box;">Professionals seeking to expand their knowledge of programming and data analysis</li><li fr-original-style="" style="box-sizing: border-box;">Business owners seeking to develop their own websites and landing pages</li><li fr-original-style="" style="box-sizing: border-box;">Students seeking to gain the talents and expertise to pursue a career in programming and data analysis</li><li fr-original-style="" style="box-sizing: border-box;">Anyone interested in developing the ability and confidence needed to succeed in the rapidly growing programming and data analysis field.</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Requirements</div>
    </h2><div id="requirements" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Anyone can enrol in this <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Programmer Complete Bundle - QLS Endorsed</strong>.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
    </h2><div id="career_path" fr-original-style="" style="box-sizing: border-box;">
                            <ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Python Developer: &pound;35,000 - &pound;75,000 per annum</li><li fr-original-style="" style="box-sizing: border-box;">Data Analyst: &pound;25,000 - &pound;45,000 per annum</li><li fr-original-style="" style="box-sizing: border-box;">Machine Learning Engineer: &pound;50,000 - &pound;100,000 per annum</li><li fr-original-style="" style="box-sizing: border-box;">Web Developer: &pound;25,000 - &pound;45,000 per annum</li><li fr-original-style="" style="box-sizing: border-box;">Software Engineer: &pound;30,000 - &pound;60,000 per annum</li><li fr-original-style="" style="box-sizing: border-box;">Business Intelligence Analyst: &pound;30,000 - &pound;65,000 per annum</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
    </h2><div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">Hard Copy QLS Certificate</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Hard copy certificate -
            Included
        </p>
        <h4 fr-original-style="" style="box-sizing: border-box;">PDF Certificate</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>
        <h4 fr-original-style="" style="box-sizing: border-box;">Student ID Card</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>

                        </div>

Imperial Academy

Machine Learning (ML)

Data Science

Python

Azure Machine Learning

Python Programmer Complete Bundle - QLS Endorsed

<strong>Learn to use SQLite databases with Python through a hands-on approach</strong>
        <p>Free and open-source, SQLite is a popular database among developers for smaller projects, hobby projects, and even testing and development. When used with Python, one of the most powerful programming languages, they make a powerful combination. 

In this course you'll learn the basics of using SQLite with Python. You'll get started with installing Python and SQLite. The course will then get you up to speed with creating databases and tables. To help you build on your knowledge further, you'll get comprehensive insights into working with tables, including adding, updating, and retrieving specific data using standard SQLite commands. Finally, you'll build a basic app to help reinforce all the things that you've learned through the course.

By the end of this course, you'll have hands-on experience in effectively using SQLite with Python.</p>
        <h2>What You Will Learn</h2><p>Explore the fundamental aspects of what a database can do<br>Install Python, SQLite, Sublime Text, and Git Bash<br>Gain practical knowledge by building an app Update and delete data from a database<br>Discover the technique of creating basic reports with data from your database</p>
        <h2>Audience</h2><p>This course is designed for anyone who wants to use SQLite databases with Python. Although a basic understanding of Python programming is assumed, knowledge of databases or SQLite is not mandatory.</p>
        <h2>Approach</h2><p>This course is packed with step-by-step instructions and a variety of practical examples.</p>
        <h2>Key Features</h2><p>Learn how to use SQLite databases with Python * Create databases and tables  * Insert, sort, and extract specific data</p>
        
        <h2>About the Author</h2><h3>John Elder</h3><p>John Elder is a pioneer in Web Development who created one of the first Internet advertising networks back in 1997. He sold it to a publicly traded company at the height of the dot com boom and then went on to create the best-selling Submission-Spider search engine submission software that's been used by over 3 million individuals and small businesses in over 45 countries.

John graduated with honors with a degree in economics from Washington University in St. Louis where he was an artsci scholar. Today he teaches Web Development courses at Codemy the online code school he founded.</p>
        <h2>Course Outline</h2><h4>1. Installation</h4><p></p><br><h4>2. Using SQLite With Python</h4><p></p><br><h4>3. Build A Simple Database App with Python</h4><p></p>

Database Programming

Learn to use SQLite databases with Python through a hands-on approach

Introduction to SQLite Databases for Python Programming

<p>Are you interested in learning one of the most popular and widely-used programming languages in the world? Look no further than our Python (Machine Learning &amp; Programming) bundle, endorsed by the Quality Licence Scheme. This comprehensive bundle is designed to provide you with a solid foundation in Python programming and machine learning, allowing you to pursue a range of exciting career paths in this rapidly-growing field.</p> <p>The Python programming course covers the fundamental concepts of programming in Python, including variables, data types, functions, and control structures. You'll also learn about object-oriented programming, file handling, and exception handling, giving you a broad understanding of how to use Python to solve real-world problems.</p> <p>In addition to Python programming, this bundle also covers machine learning, one of the most in-demand skills in the tech industry. The Machine Learning course provides a comprehensive overview of the concepts and techniques used in machine learning, including regression analysis, decision trees, and clustering. You'll also learn about data pre-processing, model selection, and evaluation, giving you the skills and knowledge needed to build and deploy machine learning models.</p> <p>Upon completing this bundle, you'll receive a hardcopy certificate endorsed by the Quality Licence Scheme, proving your Python programming and machine learning proficiency. So why wait? Enrol today and take the first step towards a rewarding and challenging career in the tech industry.</p> <p><strong>This bundle comprises the following courses:</strong></p> <p><strong>QLS Endorsed Courses:</strong></p> <ul> <li><strong>Course 01:</strong> Certificate in Machine Learning with Python at QLS Level 3</li><li><strong>Course 02:</strong> Diploma in Python Programming at QLS Level 5</li></ul> <p><strong>CPD QS Accredited Courses:</strong></p> <ul> <li><strong>Course 03:</strong> Azure Machine Learning</li><li><strong>Course 04:</strong> Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 05:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 06:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 07:</strong> 2021 Data Science &amp; Machine Learning with R from A-Z</li></ul> <p><strong>Learning Outcomes</strong></p> <ul> <li>Develop a solid understanding of Python for building applications and data manipulation.</li><li>Learn the fundamental concepts and techniques used in machine learning and data science using Python.</li><li>Acquire the skills to build and train machine learning models for predictive analysis.</li><li>Understand how to use popular tools and platforms like AWS SageMaker and Azure for machine learning.</li><li>Develop a good understanding of statistics and probability concepts used in data science and machine learning.</li><li>Gain hands-on experience in using R programming language for data science and machine learning.</li><li>Acquire a valuable skill set that can be applied to various industries such as finance, healthcare, and tech.</li></ul> <p><strong>Please Note:</strong> Imperial Academy is a One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><p>The Machine Learning with Python course is designed to provide learners with a comprehensive understanding of machine learning and data science using Python programming language. This course covers a wide range of topics, including data analysis, data visualisation, regression, classification, clustering, neural networks, and deep learning. Through this course, learners will develop the skills and knowledge to build and train machine learning models for predictive analysis.</p> <p>The Diploma in Python Programming course is designed to teach learners the fundamentals of Python programming language, including syntax, data types, control structures, functions, and modules. Learners will also learn about the different data structures and algorithms used in Python programming. This course provides a strong foundation for learners who are interested in pursuing a career in software development or data science.</p> <p><strong>Certification</strong></p> <p>In pursuing QLS-endorsed courses, learners attain not only profound understanding but also amplify their professional prowess, enriching their curriculum vitae.</p> <p><strong>The Quality Licence Scheme </strong>stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</p> <p>Investing in this comprehensive package, learners will not only receive one but TWO HARD COPY QLS ENDORSED CERTIFICATES conveniently <strong>delivered to their doorstep!</strong></p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                             <ul> <li>Students and recent graduates looking to enter the field of programming or machine learning</li><li>Professionals looking to enhance their skills in Python programming and machine learning</li><li>Individuals looking for a career change into programming or machine learning</li><li>Anyone interested in learning about Python programming and machine learning</li><li>Entrepreneurs who want to develop their own machine-learning applications</li></ul> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <ul> <li>Python Developer - £35,000 to £65,000 per year</li><li>Machine Learning Engineer - £45,000 to £85,000 per year</li><li>Data Analyst - £25,000 to £50,000 per year</li><li>Software Engineer - £30,000 to £60,000 per year</li><li>Artificial Intelligence Engineer - £50,000 to £90,000 per year</li></ul> <p>Please note that salary ranges are estimates and may vary depending on qualifications, experience and other factors.</p> 
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Level 3 & 5 Endorsed Diploma | QLS Hard Copy Certificate Included | Plus 5 CPD Courses | Lifetime Access

Python (Machine Learning & Programming)

Computer Vision

Programming

This course will help you learn the programming fundamentals with Python 3. It is designed for beginners in Python and is a complete masterclass. This course will help you understand Python GUI, data science, full-stack web development with Django, machine learning, artificial intelligence, Natural Language Processing, and Computer Vision.

Python in Practice - 15 Projects to Master Python

<strong>This comprehensive deep learning course with Python will start with the basics and work up to advanced topics such as using different frameworks in Python to solve real-world problems and building artificial neural networks with TensorFlow and Keras. </strong>
        <p>Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python's best application is in deep learning and artificial intelligence (AI)  tasks.

You'll start with an introduction to deep learning where you'll focus on the fundamentals of the deep learning theory and find out how to use deep learning in Python, before moving on to artificial neural networks (ANNs). You'll learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence. Next, you'll discover how to make predictions using linear regression, polynomial regression, and multivariate regression, and build artificial neural networks with TensorFlow and Keras. The video also covers convolutional neural networks (CNNs) at length and goes through the different components such as convolution layer, pooling layer, and fully connected layer. Finally, you'll wrap up CNN  implementation  in Python.

By the end of this course, you'll be able to use the concepts of deep learning to build neural networks in Python like a professional.</p>
        <h2>What You Will Learn</h2><p>Get to grips with the fundamentals of the deep learning theory<br>Discover how to use deep learning in Python<br>Find out how to use different frameworks in Python<br>Build artificial neural networks with TensorFlow and Keras<br>Explore the implementation of ANNs and CNNs in Python</p>
        <h2>Audience</h2><p>This course is for both programming beginners and professionals who want to expand on their knowledge of deep learning, or professional mathematicians looking to learn how to analyze data programmatically. Prerequisites include basic mathematical skills and Python coding experience.</p>
        <h2>Approach</h2><p>This fundamentals training is excellent for both newcomers and seasoned professionals. This is the best course for you if you're looking for one that starts with the fundamentals and progresses to more complex subjects. It does not teach anything but what is necessary to get started with deep learning. This is pretty much everything you need to get started with the topic, and it also helps to keep the course quite concise.</p>
        <h2>Key Features</h2><p>Fundamentals course designed for both beginners and experts alike * Use different frameworks in Python to solve real-world problems using deep learning and AI * Make predictions using linear regression, polynomial regression, and multivariate regression</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-for-Deep-Learning---Build-Neural-Networks-in-Python</p>
        <h2>About the Author</h2><h3>Meta Brains</h3><p>Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for finance, coding, and Excel. They bring together both professional and educational experiences to create world-class training programs accessible to everyone.

Currently, they're focused on the next great revolution in computing: The Metaverse. Their ultimate objective is to train the next generation of talent so that we can code and build the metaverse together!</p>
        <h2>Course Outline</h2><h4>1. Introduction to Deep Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>In this video, we will have a quick introduction to the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Deep Learning?</p><p style='margin: 0'>In this video, we will understand what deep learning is.</p></td></tr><tr><td style='padding: 15px;'><p>3. Why is Deep Learning Important?</p><p style='margin: 0'>In this video, we will understand the importance of deep learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Software and Frameworks</p><p style='margin: 0'>In this video, you will learn about software and frameworks.</p></td></tr></tbody></table><br><h4>2. Artificial Neural Networks (ANN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Introduction</p><p style='margin: 0'>In this video, we will have a quick introduction to this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Anatomy and Function of Neurons</p><p style='margin: 0'>In this video, you will learn about the anatomy and function of neurons.</p></td></tr><tr><td style='padding: 15px;'><p>3. An Introduction to The Neural Network</p><p style='margin: 0'>In this video, we will have a quick introduction to the neural network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Architecture of a Neural Network</p><p style='margin: 0'>In this video, you will learn about the architecture of a neural network.</p></td></tr></tbody></table><br><h4>3. Propagation of Information in ANNs</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Feed-Forward and Back Propagation Networks</p><p style='margin: 0'>In this video, you will learn about feed-forward and back propagation networks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Backpropagation in Neural Networks</p><p style='margin: 0'>In this video, you will learn about backpropagation in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Minimizing the Cost Function Using Backpropagation</p><p style='margin: 0'>In this video, you will learn how to minimize the cost function using backpropagation.</p></td></tr></tbody></table><br><h4>4. Neural Network Architectures</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Single Layer Perceptron (SLP) Model</p><p style='margin: 0'>In this video, you will learn about the Single-layer perceptron (SLP) model.</p></td></tr><tr><td style='padding: 15px;'><p>2. Radial Basis Network (RBN)</p><p style='margin: 0'>In this video, you will learn about Radial Basis Network (RBN).</p></td></tr><tr><td style='padding: 15px;'><p>3. Multi-Layer Perceptron (MLP) Neural Network</p><p style='margin: 0'>In this video, you will learn about multi-layer perceptron (MLP) Neural Network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Recurrent Neural Network (RNN)</p><p style='margin: 0'>In this video, you will learn about Recurrent Neural Network (RNN).</p></td></tr><tr><td style='padding: 15px;'><p>5. Long Short-Term Memory (LSTM) Networks</p><p style='margin: 0'>In this video, you will learn about Long Short-Term Memory (LSTM) networks.</p></td></tr><tr><td style='padding: 15px;'><p>6. Hopfield Neural Network</p><p style='margin: 0'>In this video, you will learn about Hopfield neural network.</p></td></tr><tr><td style='padding: 15px;'><p>7. Boltzmann Machine Neural Network</p><p style='margin: 0'>In this video, you will learn about Boltzmann Machine Neural Network.</p></td></tr></tbody></table><br><h4>5. Activation Functions</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is the Activation Function?</p><p style='margin: 0'>In this video, we will understand what the Activation function is.</p></td></tr><tr><td style='padding: 15px;'><p>2. Important Terminologies</p><p style='margin: 0'>In this video, we will understand some important terminologies like differential function and monotonic function.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Sigmoid Function</p><p style='margin: 0'>In this video, you will learn about the Sigmoid function.</p></td></tr><tr><td style='padding: 15px;'><p>4. Hyperbolic Tangent Function</p><p style='margin: 0'>In this video, you will learn about the Hyperbolic tangent function.</p></td></tr><tr><td style='padding: 15px;'><p>5. SoftMax Function</p><p style='margin: 0'>In this video, you will learn about the SoftMax function.</p></td></tr><tr><td style='padding: 15px;'><p>6. Rectified Linear Unit (ReLU) Function</p><p style='margin: 0'>In this video, you will learn about Rectified Linear Unit (ReLU) function.</p></td></tr><tr><td style='padding: 15px;'><p>7. Leaky Rectified Linear Unit function</p><p style='margin: 0'>In this video, you will learn about the Leaky Rectified Linear Unit function.</p></td></tr></tbody></table><br><h4>6. Gradient Descent Algorithm</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Gradient Descent?</p><p style='margin: 0'>In this video, we will understand what Gradient Descent is.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Stochastic Gradient Descent?</p><p style='margin: 0'>In this video, we will understand what is Stochastic Gradient Descent?</p></td></tr><tr><td style='padding: 15px;'><p>3. Gradient Descent versus Stochastic Gradient Descent</p><p style='margin: 0'>In this video, you will learn about Gradient Decent versus Stochastic Gradient Descent.</p></td></tr></tbody></table><br><h4>7. Summary - Overview of Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How do Artificial Neural Networks Work?</p><p style='margin: 0'>In this video, we will understand how artificial neural networks work.</p></td></tr><tr><td style='padding: 15px;'><p>2. Advantages of Neural Networks</p><p style='margin: 0'>In this video, you will learn about the advantages of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Disadvantages of Neural Networks</p><p style='margin: 0'>In this video, you will learn about the disadvantages of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>4. Applications of Neural Networks</p><p style='margin: 0'>In this video, you will learn about the applications of neural networks.</p></td></tr></tbody></table><br><h4>8. Implementation of ANN in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will have a quick Introduction to this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Exploring the Dataset</p><p style='margin: 0'>In this video, you will learn how to explore the dataset.</p></td></tr><tr><td style='padding: 15px;'><p>3. Problem Statement</p><p style='margin: 0'>In this video, we will understand the problem statement.</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Pre-Processing</p><p style='margin: 0'>In this video, you will learn how data pre-processing works.</p></td></tr><tr><td style='padding: 15px;'><p>5. Loading the Dataset</p><p style='margin: 0'>In this video, you will learn how to load the dataset.</p></td></tr><tr><td style='padding: 15px;'><p>6. Splitting the Dataset into Independent and Dependent Variables</p><p style='margin: 0'>In this video, you will learn how to split the dataset into independent and dependent variables.</p></td></tr><tr><td style='padding: 15px;'><p>7. Label Encoding Using Scikit-Learn</p><p style='margin: 0'>In this video, you will learn how to work with label encoding using scikit-learn</p></td></tr><tr><td style='padding: 15px;'><p>8. One-hot encoding using scikit-learn</p><p style='margin: 0'>In this video, we will learn how to work with One-hot encoding using scikit-learn.</p></td></tr><tr><td style='padding: 15px;'><p>9. Training and Test Sets: Splitting Data</p><p style='margin: 0'>In this video, we will be working on splitting the data with training and test sets.</p></td></tr><tr><td style='padding: 15px;'><p>10. Feature Scaling</p><p style='margin: 0'>In this video, you will learn about feature scaling.</p></td></tr><tr><td style='padding: 15px;'><p>11. Building the Artificial Neural Network</p><p style='margin: 0'>In this video, you will learn how to build the Artificial Neural Network</p></td></tr><tr><td style='padding: 15px;'><p>12. Adding the Input Layer and the First Hidden Layer</p><p style='margin: 0'>In this video, you will learn how to add the input layer and the first hidden layer.</p></td></tr><tr><td style='padding: 15px;'><p>13. Adding the Next Hidden Layer</p><p style='margin: 0'>In this video, you will learn how to add the next hidden layer.</p></td></tr><tr><td style='padding: 15px;'><p>14. Adding the Output Layer</p><p style='margin: 0'>In this video, you will learn how to add the output layer.</p></td></tr><tr><td style='padding: 15px;'><p>15. Compiling the Artificial Neural Network</p><p style='margin: 0'>In this video, you will learn how to compile the Artificial Neural Network.</p></td></tr><tr><td style='padding: 15px;'><p>16. Fitting the ANN Model to the Training Set</p><p style='margin: 0'>In this video, you will learn how to fit the ANN model into the training set.</p></td></tr><tr><td style='padding: 15px;'><p>17. Predicting the Test Set Results</p><p style='margin: 0'>In this video, you will learn how to predict the test set results.</p></td></tr></tbody></table><br><h4>9. Convolutional Neural Networks (CNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will have a quick Introduction to the learning goals for this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Components of Convolutional Neural Networks</p><p style='margin: 0'>In this video, you will learn about the components of Convolutional Neural Networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Convolution Layer</p><p style='margin: 0'>In this video, you will learn about the Convolution layer.</p></td></tr><tr><td style='padding: 15px;'><p>4. Pooling Layer</p><p style='margin: 0'>In this video, you will learn about Pooling layer.</p></td></tr><tr><td style='padding: 15px;'><p>5. Fully Connected Layer</p><p style='margin: 0'>In this video, you will learn about fully connected layer.</p></td></tr></tbody></table><br><h4>10. Implementation of CNN in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Dataset</p><p style='margin: 0'>In this video, we are going to use the MNIST handwritten digit data which contains the images of handwritten digits.</p></td></tr><tr><td style='padding: 15px;'><p>2. Importing Libraries</p><p style='margin: 0'>In this video, you will learn how to import libraries.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building the CNN Model</p><p style='margin: 0'>In this video, you will learn how to build the Convolutional Neural Networks (CNN) model.</p></td></tr><tr><td style='padding: 15px;'><p>4. Accuracy of the Model</p><p style='margin: 0'>In this video, you will learn about the accuracy of the Convolutional Neural Networks (CNN) model.</p></td></tr></tbody></table>

TensorFlow

This comprehensive deep learning course with Python will start with the basics and work up to advanced topics such as using different frameworks in Python to solve real-world problems and building artificial neural networks with TensorFlow and Keras.

Python for Deep Learning - Build Neural Networks in Python

<strong>Python programming and Ruby coding are popular for a reason! Become a software coder, web developer, or hacker today</strong>
        <p>In this course, you'll learn the Python 3 programming language and the Ruby programming language from an absolute beginner level to advanced-in no time at all! This course targets absolute beginners; you don't need any coding experience at all!

We start by setting up, downloading, and installing Python and the Sublime Text Editor-both for free. This will give you all the tools you need to start writing and running Python code. 

For Ruby, we'll use a free online development environment (or you can download Ruby yourself onto your computer).

Then, we'll delve into very basic computer science concepts, such as:
&bull;	Printing to the screen
&bull;	String manipulation
&bull;	Variables
&bull;	Data types (lists, tuples, dictionaries and more)
&bull;	Simple math
&bull;	Comparison operators
&bull;	Assignment operators

After that, we'll move on to more intermediate topics such as:
&bull;	Conditional if/else statements
&bull;	While loops
&bull;	For loops
&bull;	Fizzbuzz

Finally, we'll finish up with more advanced topics like:
&bull;	Functions
&bull;	Modules
&bull;	Classes

We'll learn all these things for both Python and Ruby. We start with Python, and then on to Ruby in the second half of the course.</p>
        <h2>What You Will Learn</h2><p>Learn actual, applicable programming concepts.<br>Understand basic topics such as variables, data types, conditional if/else and comparisons, and while/for loops</p>
        <h2>Audience</h2><p>This course targets absolute beginners who want to learn the Python and Ruby programming languages.</p>
        <h2>Approach</h2><p>This course is a step-by-step approach, just follow along with the instructor and build what he builds. Learn as you build with this hands-on video series.</p>
        <h2>Key Features</h2><p>Learn Python from beginner to advanced * Understand programming concepts applicable to any programming language * Learn Ruby from beginner to advanced</p>
        
        <h2>About the Author</h2><h3>John Elder</h3><p>John Elder is a pioneer in Web Development who created one of the first Internet advertising networks back in 1997. He sold it to a publicly traded company at the height of the dot com boom and then went on to create the best-selling Submission-Spider search engine submission software that's been used by over 3 million individuals and small businesses in over 45 countries.

John graduated with honors with a degree in economics from Washington University in St. Louis where he was an artsci scholar. Today he teaches Web Development courses at Codemy the online code school he founded.</p>
        <h2>Course Outline</h2><h4>1. Beginners Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Intro - Python 3 vs Python 2 - Which Should You Use?</p><p style='margin: 0'>Beginners Python: Intro - Python 3 vs Python 2 - Which Should You Use?</p></td></tr><tr><td style='padding: 15px;'><p>2. Installing Python</p><p style='margin: 0'>Beginners Python: Installing Python</p></td></tr><tr><td style='padding: 15px;'><p>3. First Program - Hello World!</p><p style='margin: 0'>Beginners Python: First Program - Hello World!</p></td></tr><tr><td style='padding: 15px;'><p>4. Comments and Clearing the Screen</p><p style='margin: 0'>Beginners Python: Comments and Clearing the Screen</p></td></tr><tr><td style='padding: 15px;'><p>5. Variables</p><p style='margin: 0'>Beginners Python: Variables</p></td></tr><tr><td style='padding: 15px;'><p>6. Intro to Data Types</p><p style='margin: 0'>Beginners Python: Intro to Data Types</p></td></tr><tr><td style='padding: 15px;'><p>7. Strings</p><p style='margin: 0'>Beginners Python: Strings</p></td></tr><tr><td style='padding: 15px;'><p>8. String Manipulation</p><p style='margin: 0'>Beginners Python: String Manipulation</p></td></tr><tr><td style='padding: 15px;'><p>9. Math with Python</p><p style='margin: 0'>Beginners Python: Math with Python</p></td></tr><tr><td style='padding: 15px;'><p>10. Floats and Ints</p><p style='margin: 0'>Beginners Python: Floats and Ints</p></td></tr><tr><td style='padding: 15px;'><p>11. Assignment Operators</p><p style='margin: 0'>Beginners Python: Assignment Operators</p></td></tr><tr><td style='padding: 15px;'><p>12. Lists</p><p style='margin: 0'>Beginners Python: Lists</p></td></tr><tr><td style='padding: 15px;'><p>13. Tuples</p><p style='margin: 0'>Beginners Python: Tuples</p></td></tr><tr><td style='padding: 15px;'><p>14. Dictionaries</p><p style='margin: 0'>Beginners Python: Dictionaries</p></td></tr></tbody></table><br><h4>2. Intermediate Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Comparison Operators</p><p style='margin: 0'>Intermediate Python: Comparison Operators</p></td></tr><tr><td style='padding: 15px;'><p>2. Conditional Statements</p><p style='margin: 0'>Intermediate Python: Conditional Statements</p></td></tr><tr><td style='padding: 15px;'><p>3. Multiple Conditional Statements</p><p style='margin: 0'>Intermediate Python: Multiple Conditional Statements</p></td></tr><tr><td style='padding: 15px;'><p>4. While Loops</p><p style='margin: 0'>Intermediate Python: While Loops</p></td></tr><tr><td style='padding: 15px;'><p>5. For Loops</p><p style='margin: 0'>Intermediate Python: For Loops</p></td></tr><tr><td style='padding: 15px;'><p>6. Let's Build FizzBuzz!</p><p style='margin: 0'>Intermediate Python: Let's Build FizzBuzz!</p></td></tr></tbody></table><br><h4>3. Advanced Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Functions</p><p style='margin: 0'>Advanced Python: Functions</p></td></tr><tr><td style='padding: 15px;'><p>2. Functions Part 2</p><p style='margin: 0'>Advanced Python: Functions Part 2</p></td></tr><tr><td style='padding: 15px;'><p>3. Modules</p><p style='margin: 0'>Advanced Python: Modules</p></td></tr><tr><td style='padding: 15px;'><p>4. Intro To Classes</p><p style='margin: 0'>Advanced Python: Intro To Classes</p></td></tr><tr><td style='padding: 15px;'><p>5. Classes Part 1</p><p style='margin: 0'>Advanced Python: Classes Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Classes Part 2</p><p style='margin: 0'>Advanced Python: Classes Part 2</p></td></tr><tr><td style='padding: 15px;'><p>7. Classes Part 3</p><p style='margin: 0'>Advanced Python: Classes Part 3</p></td></tr><tr><td style='padding: 15px;'><p>8. Course Conclusion</p><p style='margin: 0'>Advanced Python: Course Conclusion</p></td></tr></tbody></table><br><h4>4. Ruby Programming Basics</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ruby Programming Basics: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Install Sublime Text and Git Bash</p><p style='margin: 0'>Ruby Programming Basics: Install Sublime Text and Git Bash</p></td></tr><tr><td style='padding: 15px;'><p>3. Install Ruby</p><p style='margin: 0'>Ruby Programming Basics: Install Ruby</p></td></tr><tr><td style='padding: 15px;'><p>4. Hello World! First Program</p><p style='margin: 0'>Ruby Programming Basics: Hello World! First Program</p></td></tr><tr><td style='padding: 15px;'><p>5. Printing To The Screen With Puts and Print</p><p style='margin: 0'>Ruby Programming Basics: Printing To The Screen With Puts and Print</p></td></tr><tr><td style='padding: 15px;'><p>6. String Manipulation</p><p style='margin: 0'>Ruby Programming Basics: String Manipulation</p></td></tr><tr><td style='padding: 15px;'><p>7. Variables</p><p style='margin: 0'>Ruby Programming Basics: Variables</p></td></tr><tr><td style='padding: 15px;'><p>8. Getting User Input with Gets</p><p style='margin: 0'>Ruby Programming Basics: Getting User Input with Gets</p></td></tr><tr><td style='padding: 15px;'><p>9. Simple Math</p><p style='margin: 0'>Ruby Programming Basics: Simple Math</p></td></tr><tr><td style='padding: 15px;'><p>10. Converting to An Integer</p><p style='margin: 0'>Ruby Programming Basics: Converting to An Integer</p></td></tr><tr><td style='padding: 15px;'><p>11. Comparison Operators</p><p style='margin: 0'>Ruby Programming Basics: Comparison Operators</p></td></tr><tr><td style='padding: 15px;'><p>12. Assignment Operators</p><p style='margin: 0'>Ruby Programming Basics: Assignment Operators</p></td></tr><tr><td style='padding: 15px;'><p>13. If/Else Conditional Statements</p><p style='margin: 0'>Ruby Programming Basics: If/Else Conditional Statements</p></td></tr></tbody></table><br><h4>5. Intermediate Ruby Programming</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Arrays</p><p style='margin: 0'>Intermediate Ruby Programming: Arrays</p></td></tr><tr><td style='padding: 15px;'><p>2. While Loops</p><p style='margin: 0'>Intermediate Ruby Programming: While Loops</p></td></tr><tr><td style='padding: 15px;'><p>3. Each Loops</p><p style='margin: 0'>Intermediate Ruby Programming: Each Loops</p></td></tr><tr><td style='padding: 15px;'><p>4. Hashes</p><p style='margin: 0'>Intermediate Ruby Programming: Hashes</p></td></tr><tr><td style='padding: 15px;'><p>5. Building FizzBuzz!</p><p style='margin: 0'>Intermediate Ruby Programming: Building FizzBuzz!</p></td></tr><tr><td style='padding: 15px;'><p>6. Methods Part 1</p><p style='margin: 0'>Intermediate Ruby Programming: Methods Part 1</p></td></tr><tr><td style='padding: 15px;'><p>7. Methods Part 2</p><p style='margin: 0'>Intermediate Ruby Programming: Methods Part 2</p></td></tr></tbody></table><br><h4>6. Advanced Ruby Programming</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Intro to Ruby Classes</p><p style='margin: 0'>Advanced Ruby Programming: Intro to Ruby Classes</p></td></tr><tr><td style='padding: 15px;'><p>2. Ruby Classes Part 1</p><p style='margin: 0'>Advanced Ruby Programming: Ruby Classes Part 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Class Getters</p><p style='margin: 0'>Advanced Ruby Programming: Class Getters</p></td></tr><tr><td style='padding: 15px;'><p>4. Class Setters</p><p style='margin: 0'>Advanced Ruby Programming: Class Setters</p></td></tr><tr><td style='padding: 15px;'><p>5. Adding Perimeter and Area Methods</p><p style='margin: 0'>Advanced Ruby Programming: Adding Perimeter and Area Methods</p></td></tr><tr><td style='padding: 15px;'><p>6. Class Attr Accessor</p><p style='margin: 0'>Advanced Ruby Programming: Class Attr Accessor</p></td></tr><tr><td style='padding: 15px;'><p>7. Creating a Draw Method</p><p style='margin: 0'>Advanced Ruby Programming: Creating a Draw Methods</p></td></tr></tbody></table>

Ruby

Python programming and Ruby coding are popular for a reason! Become a software coder, web developer, or hacker today

Python and Ruby Programming Bundle

<strong>This intermediate-level course will help you learn how to use multi-threading and asynchronous programming to speed up programs that are heavily bottlenecked by IO operations. The course covers core concepts such as implementing multiprocessing in Python, creating various readers and schedulers, and monitoring your coding progress.</strong>
        <p>In a big data project, a plethora of information is retrieved, big numbers are crunched on our machine, or both. If the coding is sequential or synchronous, our application will struggle to execute. Two mechanisms to alleviate such bottlenecks are concurrency and parallelism. In Python, concurrency is represented by threading, whereas multiprocessing achieves parallelism.

This course begins with an introduction about potential programming speed bottlenecks and solving them. You will delve into Python concepts and create a Wikipedia Reader, Yahoo Finance Reader, Queues, and Master Scheduler. You will build a multi-threaded program to grab data from the Internet and parse and save them into a local database. Implement multiprocessing in Python, which lets us use multiple CPUs in our code. Learn about threading, multiprocessing, asynchronous wait, locking, multiprocessing queues, Pool Map Multiple Arguments, writing asynchronous programs, and combining async and multiprocessing.

Upon completion, we can spread our workload over all cores available on the used machine. We will combine both elements, multiprocessing with asynchronous programming, to maximize benefit and CPU resource usage and minimize the time spent waiting for IO responses. You will create multi-threaded, asynchronous, multi-process programs to make programs run faster.

All resources are available at: https://github.com/PacktPublishing/Concurrent-and-Parallel-Programming-in-Python</p>
        <h2>What You Will Learn</h2><p>Learn to use concurrency and parallelism in Python<br>Write multi-threaded programs in Python to reduce coding lengths<br>Write multi-process programs that execute even faster<br>Understand the differences between concurrency and parallelism<br>Create asynchronous programs in Python by adding concurrency<br>Spread workload over all the cores available on a machine being used</p>
        <h2>Audience</h2><p>This course is aimed at intermediate- to mastery-level seeking programmers, API developers, web developers, and application developers who know basic- to intermediate-level Python coding beforehand. The topics on concurrency and parallelism expect one to be aware of basic to intermediate understanding of coding on Python. Prior knowledge of basic Python coding is desirable for optimal benefit from this course.</p>
        <h2>Approach</h2><p>The author delivers the courses in an online classroom format using high-quality onscreen video lectures and PowerPoint presentations. Each class begins with a quick refresher to the previously learned topic followed by an engaging and interactive video session and a summarization at the end of each lecture, thus enabling the learner to hone their skillsets learned efficiently.</p>
        <h2>Key Features</h2><p>Understand where potential computing speed bottlenecks arise and how we can resolve them * Create multi-threaded, asynchronous, and multi-process programs to make your programs run faster * Combine multiprocessing with asynchronous programming to get maximum benefits of coding</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Concurrent-and-Parallel-Programming-in-Python</p>
        <h2>About the Author</h2><h3>Maximilian Schallwig</h3><p>Maximilian Schallwig is a data engineer and a proficient Python programmer. He holds a bachelor's degree in physics and a master's degree in astrophysics. He has been working on data for over five years, first as a data scientist and then as a data engineer. He can talk endlessly about big data pipelines, data infrastructure, and his unwavering devotion to Python.

Even after two unsuccessful attempts in high school, he still decided to learn Python at the University. He cautiously stepped into the realm of data, beginning with a simple Google search for 'what does a data scientist do'.

He was determined to pursue a career in data science to become a data engineer by learning about big data tools and infrastructure design to build scalable systems and pipelines. He enjoys sharing his programming skills with the rest of the world.</p>
        <h2>Course Outline</h2><h4>1. Threading</h4><p>This section focuses on concurrency, and we will get introduced to threading, multiprocessing, and asynchronous coding. You will also learn to create threading classes, different kinds of readers and schedulers, and monitor our coding progress.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Threading, Multiprocessing, Async Introduction</p><p style='margin: 0'>In this video, we will be introduced to threading, multiprocessing, and asynchronous programs and the different considerations of choosing Python for coding.</p></td></tr><tr><td style='padding: 15px;'><p>2. Threading in Python</p><p style='margin: 0'>This lecture demonstrates how to write our first concurrent program. The author also explains the benefits of choosing the Python coding portal instead of the other code-writing portals.</p></td></tr><tr><td style='padding: 15px;'><p>3. Creating a Threading Class</p><p style='margin: 0'>After learning how to write our first threading program, you will learn how to use classes as our threading tools.</p></td></tr><tr><td style='padding: 15px;'><p>4. Creating a Wikipedia Reader</p><p style='margin: 0'>After comprehensively practicing our basic threading creation, we will look at the project we will create to implement threading more effectively.</p></td></tr><tr><td style='padding: 15px;'><p>5. Creating a Yahoo Finance Reader</p><p style='margin: 0'>After learning how to create the Wikipedia reader, you will learn how to write a threaded class to extract the price from a Yahoo Finance portal.</p></td></tr><tr><td style='padding: 15px;'><p>6. Queues and Master Scheduler</p><p style='margin: 0'>In the previous lessons, you learned how to set up our Wikipedia reader and the Yahoo Finance worker; we will now systemize the process and create separate process entities. You will learn to create queues, take elements, and place them in a queue.</p></td></tr><tr><td style='padding: 15px;'><p>7. Creating a Postgres Worker</p><p style='margin: 0'>In this lesson, we will create our instruction worker using a Postgres database.</p></td></tr><tr><td style='padding: 15px;'><p>8. Integrating the Postgres Worker</p><p style='margin: 0'>After creating our Postgres database insertion worker, we will integrate this worker into our main function and make changes to ensure that everything is working properly.</p></td></tr><tr><td style='padding: 15px;'><p>9. Yaml File Introduction</p><p style='margin: 0'>After creating a working program, you will learn how to clean up the appearance and make it nicer to have an overview and be able to expand its features in the future.</p></td></tr><tr><td style='padding: 15px;'><p>10. Creating a Yaml Reader</p><p style='margin: 0'>Now that we have the Yaml pipeline defined, we will implement something to read this file or any other file that has a queue worker structure to create our pipeline for us.</p></td></tr><tr><td style='padding: 15px;'><p>11. Improving Our Wiki Worker</p><p style='margin: 0'>After successfully creating and defining our Yaml reader, we will implement this into our main class.</p></td></tr><tr><td style='padding: 15px;'><p>12. Improving All Workers and Adding Monitoring</p><p style='margin: 0'>In this lecture, you will learn to clean up the Yaml pipeline executor, load the pipeline, initialize it, and join it to your main class.</p></td></tr><tr><td style='padding: 15px;'><p>13. Final Program Cleanup</p><p style='margin: 0'>Apart from a final cleanup to make our project more functional, you will learn to create local environment variables for actual testing.</p></td></tr><tr><td style='padding: 15px;'><p>14. Locking</p><p style='margin: 0'>This lecture will cover the locking process and things we need to vary when threading.</p></td></tr></tbody></table><br><h4>2. Multiprocessing</h4><p>This section focuses on the multiprocessing functions in Python. You will learn to create multiprocessing queues and pools and learn all about arguments and checking elements in ranges.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Multiprocessing Introduction</p><p style='margin: 0'>Here, we will understand multiprocessing and monitor how our CPU functions during multiprocessing.</p></td></tr><tr><td style='padding: 15px;'><p>2. Multiprocessing Queues</p><p style='margin: 0'>Now that we have understood multiprocessing, you will learn how to split our segments over multiple processes in the form of queues to speed up processing and take a load off the process execution.</p></td></tr><tr><td style='padding: 15px;'><p>3. Multiprocessing Pool</p><p style='margin: 0'>After learning how to distribute the work by multiprocessing over different CPUs, you will learn to replicate the process over a multiprocessing program surrounding multiprocessing classes.</p></td></tr><tr><td style='padding: 15px;'><p>4. Multiprocessing Pool Map Multiple Arguments</p><p style='margin: 0'>Let's learn how to pass multiple arguments, passing lists instead of elements in a list or class while executing a code.</p></td></tr><tr><td style='padding: 15px;'><p>5. Multiprocessing Multiple Varying Arguments</p><p style='margin: 0'>In the previous video, we have seen how to provide additional arguments using a partial library in the functools built-in tool; you will now learn to add different elements such as a power list instead.</p></td></tr><tr><td style='padding: 15px;'><p>6. Multiprocessing Checking Elements in List in Certain Ranges</p><p style='margin: 0'>In this video, we will finally revisit the initially more CPU-intensive functions we were using, which will be checking how many values in our comparison list are in a specific range.</p></td></tr></tbody></table><br><h4>3. Asynchronous</h4><p>This section focuses on the asynchronous programming aspect of Python. You will learn to write asynchronous programs, perform asynchronous tasks, use asynchronous libraries, and combine async and multiprocessing.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Writing Asynchronous Programs</p><p style='margin: 0'>In this video, we will understand what asynchronous programming is and the fundamental differences between synchronous and asynchronous programming. We will also write our first asynchronous program.</p></td></tr><tr><td style='padding: 15px;'><p>2. Asynchronous Tasks</p><p style='margin: 0'>Here, you will learn to assign asynchronous functions to tasks to execute on the asynchronous syntax.</p></td></tr><tr><td style='padding: 15px;'><p>3. Async Gather Method</p><p style='margin: 0'>Let's understand how to combine two APIs using the gather method with the await function. Here, the function waits for all tasks to complete before executing the next task assigned.</p></td></tr><tr><td style='padding: 15px;'><p>4. Using Async Timeouts</p><p style='margin: 0'>This video will explain what a timeout does in asynchronous programming and how to place timeouts in our syntax.</p></td></tr><tr><td style='padding: 15px;'><p>5. Creating Asynchronous For Loops</p><p style='margin: 0'>This lesson elaborates on what a for loop does and does not do in an asynchronous program code.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using Asynchronous Libraries</p><p style='margin: 0'>Here, you will learn how some libraries support asynchronous libraries, and others do not. You will also learn to look for those libraries and compare them.</p></td></tr><tr><td style='padding: 15px;'><p>7. The Async Wait Statement</p><p style='margin: 0'>In this video, we will look at how we can wait for specific tasks to complete before we execute the code we want to run. Unlike the gather function that waits for all tasks to complete before running a code, this statement waits for specific tasks to complete.</p></td></tr><tr><td style='padding: 15px;'><p>8. Combining Async and Multiprocessing</p><p style='margin: 0'></p></td></tr></tbody></table>

This intermediate-level course will help you learn how to use multi-threading and asynchronous programming to speed up programs that are heavily bottlenecked by IO operations. The course covers core concepts such as implementing multiprocessing in Python, creating various readers and schedulers, and monitoring your coding progress.

Concurrent and Parallel Programming in Python

<strong>Master Data Science skills using Python and real time project and go from Beginner to Super Advance level</strong>
        <p>Want to become a good Data Scientist?  Then this is a right course for you.
This course was designed by IT professionals with a Master's in Mathematics and Data Science.  We cover complex theories, algorithms, and coding libraries in a very simple way so they can be easily grasped by any beginner.
We walk you step-by-step through the World of Data science. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advanced level.
All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Data-Statistics-with-Full-Stack-Python</p>
        <h2>What You Will Learn</h2><p>Master Data Science on Python<br>Learn to use Numpy and Pandas for Data Analysis<br>Learn all the math you need to understand Machine Learning algorithms<br>Real-world case studies<br>Learn to use Matplotlib for Python plotting<br>Learn to use Seaborn for statistical plots<br>Master end-to-end data science solutions<br>Learn all statistical concepts you need to become a Machine Learning ninja </p>
        <h2>Audience</h2><p>This course is for anyone who wants to become a Data Scientist.</p>
        <h2>Approach</h2><p>Exhaustive and packed with step-by-step instructions, working examples, and helpful advice, this course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.</p>
        <h2>Key Features</h2><p>Master complex theories, algorithms, and coding libraries in a very simple way * Master Data Science in Python</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/data-statistics-with-full-stack-python</p>
        <h2>About the Author</h2><h3>Geekshub Pvt. Ltd. </h3><p>Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated real-world case studies that calibrate the learning experience of students.</p>
        <h2>Course Outline</h2><h4>1. Python Fundamentals</h4><p></p><br><h4>2. Numpy</h4><p></p><br><h4>3. Pandas</h4><p></p><br><h4>4. Some Fun With Maths</h4><p></p><br><h4>5. Inferential Statistics</h4><p></p><br><h4>6. Hypothesis Testing</h4><p></p><br><h4>7. Data Visualization</h4><p></p><br><h4>8. Exploratory Data Analysis</h4><p></p><br><h4>9. Simple Linear Regression</h4><p></p><br><h4>10. Real World Problem - Investment Requirement Analysis for a Company</h4><p></p><br><h4>11. Loan Analysis Project</h4><p></p>

Statistics

Master Data Science skills using Python and real time project and go from Beginner to Super Advance level

Data Statistics with Full Stack Python

<strong>Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis</strong>
        <p>In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in Python, you can give your company a competitive edge and level up in your career. This course will give you a robust grounding in clustering and classification, the main aspects of machine learning.

The course consists of 7 sections that will help you master Python machine learning. You'll begin with an introduction to Python data science and Anaconda, which is a powerful Python-driven framework for data science. Next, you'll delve into Pandas and read data structures, including CSV, Excel, and HTML data. As you advance, you'll perform data cleaning and munging to remove NAs\no data and discover how to handle conditional data, group by attributes, and do much more. You'll also grasp basic concepts of unsupervised learning such as K-means clustering and its implementation on the Iris dataset. The course will take you through the theory of dimension reduction and feature selection for machine learning and help you understand Principal Component Analysis (PCA) using two case studies. You'll get to grips with the linear and non-linear classification of SVM along with Gradient Boosting Machine (GBM) and Naive Bayes Classification. Finally, you'll explore neural networks and discover the powerful H20 framework and for deep learning classification. Additionally, you'll learn about perceptrons and Artificial Neural Networks (ANN) for binary classification.
By the end of this course, you'll be able to use packages such as NumPy, Pandas, and Matplotlib to work with real data in Python.

All code and supporting files for this course are available at 
https://github.com/sanjanapackt/PacktPublishing-Clustering-and-Classification-with-Machine-Learning-in-Python.</p>
        <h2>What You Will Learn</h2><p>Harness the power of Anaconda/iPython for practical data science<br>Read data into the Python environment from different sources<br>Carry out basic data preprocessing and wrangling in Python<br>Implement unsupervised/clustering techniques such as k-means clustering<br>Get to grips with dimensionality reduction techniques and feature selection<br>Implement supervised learning/classification techniques such as random forests<br>Explore neural network- and deep learning-based classification<br></p>
        <h2>Audience</h2><p>If you want to build data science applications in the Python environment, this book is for you. You'll also find this book helpful if you want to learn how to implement unsupervised learning on real data using Python, or if you're a student looking to get started with artificial neural networks and deep learning.</p>
        <h2>Approach</h2><p>This course is full of interesting and illustrative examples and easy-to-understand theory to help you implement a real-world concept in every lecture. It covers hands-on methods to simplify and address even the most difficult concepts in Python using minimal jargon.</p>
        <h2>Key Features</h2><p>Explore the most important Python data science concepts and packages, including Pandas * Master the Anaconda framework and use it to implement clustering and classification models on your data * Get to grips with data science fundamentals and understand which models should be used when * </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/clustering-and-classification-with-machine-learning-in-r</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to Clustering & Classification with Machine Learning in Python</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Welcome to Clustering & Classification with Machine Learning in Python</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning?</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: What is Machine Learning?</p></td></tr><tr><td style='padding: 15px;'><p>3. Python Data Science Environment</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Python Data Science Environment</p></td></tr><tr><td style='padding: 15px;'><p>4. For Mac Users</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: For Mac Users</p></td></tr><tr><td style='padding: 15px;'><p>5. IPython in Browser</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: IPython in Browser</p></td></tr><tr><td style='padding: 15px;'><p>6. Python Data Science Packages to Be Used</p><p style='margin: 0'>INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools: Python Data Science Packages to Be Used</p></td></tr></tbody></table><br><h4>2. Read in Data from Different Sources with Pandas</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are Pandas?</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: What are Pandas?</p></td></tr><tr><td style='padding: 15px;'><p>2. Read in Data from CSV</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Data from CSV</p></td></tr><tr><td style='padding: 15px;'><p>3. Read in Online CSV</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Online CSV</p></td></tr><tr><td style='padding: 15px;'><p>4. Read in Excel Data</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Excel Data</p></td></tr><tr><td style='padding: 15px;'><p>5. Read in HTML Data</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in HTML Data</p></td></tr><tr><td style='padding: 15px;'><p>6. Read in Data from Databases</p><p style='margin: 0'>Read in Data from Different Sources with Pandas: Read in Data from Databases</p></td></tr></tbody></table><br><h4>3. Data Cleaning & Munging</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Remove Missing Values</p><p style='margin: 0'>Data Cleaning & Munging: Remove Missing Values</p></td></tr><tr><td style='padding: 15px;'><p>2. Conditional Data Selection</p><p style='margin: 0'>Data Cleaning & Munging: Conditional Data Selection</p></td></tr><tr><td style='padding: 15px;'><p>3. Data Grouping</p><p style='margin: 0'>Data Cleaning & Munging: Data Grouping</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Sub setting</p><p style='margin: 0'>Data Cleaning & Munging: Data sub setting</p></td></tr><tr><td style='padding: 15px;'><p>5. Ranking & Sorting</p><p style='margin: 0'>Data Cleaning & Munging: Ranking & Sorting</p></td></tr><tr><td style='padding: 15px;'><p>6. Concatenate</p><p style='margin: 0'>Data Cleaning & Munging: Concatenate</p></td></tr><tr><td style='padding: 15px;'><p>7. Merging & Joining Data Frames</p><p style='margin: 0'>Data Cleaning & Munging: Merging & Joining Data Frames</p></td></tr></tbody></table><br><h4>4. Unsupervised Learning in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Unsupervised Classification- Some Basic Concepts</p><p style='margin: 0'>Unsupervised Learning in Python: Unsupervised Classification- Some Basic Concepts</p></td></tr><tr><td style='padding: 15px;'><p>2. K-Means Clustering: Theory</p><p style='margin: 0'>Unsupervised Learning in Python: K-Means Clustering: Theory</p></td></tr><tr><td style='padding: 15px;'><p>3. Implement K-Means on the Iris Data</p><p style='margin: 0'>Unsupervised Learning in Python: Implement K-Means on the Iris Data</p></td></tr><tr><td style='padding: 15px;'><p>4. Quantifying K-Means Clustering Performance</p><p style='margin: 0'>Unsupervised Learning in Python: Quantifying K-Means Clustering Performance</p></td></tr><tr><td style='padding: 15px;'><p>5. K-Means Clustering with Real Data</p><p style='margin: 0'>Unsupervised Learning in Python: K-Means Clustering with Real Data</p></td></tr><tr><td style='padding: 15px;'><p>6. How to Select the Optimal Number of Clusters?</p><p style='margin: 0'>Unsupervised Learning in Python: How to Select the Optimal Number of Clusters?</p></td></tr><tr><td style='padding: 15px;'><p>7. Gaussian Mixture Modelling (GMM)</p><p style='margin: 0'>Unsupervised Learning in Python: Gaussian Mixture Modelling (GMM)</p></td></tr><tr><td style='padding: 15px;'><p>8. Hierarchical Clustering-theory</p><p style='margin: 0'>Unsupervised Learning in Python: Hierarchical Clustering-theory</p></td></tr><tr><td style='padding: 15px;'><p>9. Hierarchical Clustering-practical</p><p style='margin: 0'>Unsupervised Learning in Python: Hierarchical Clustering-practical</p></td></tr></tbody></table><br><h4>5. Dimension Reduction & Feature Selection for Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Principal Component Analysis (PCA)-Theory</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Theory</p></td></tr><tr><td style='padding: 15px;'><p>2. Principal Component Analysis (PCA)-Case Study 1</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Case Study 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Principal Component Analysis (PCA)-Case Study 2</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Principal Component Analysis (PCA)-Case Study 2</p></td></tr><tr><td style='padding: 15px;'><p>4. Linear Discriminant Analysis (LDA) for Dimension Reduction</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Linear Discriminant Analysis (LDA) for Dimension Reduction</p></td></tr><tr><td style='padding: 15px;'><p>5. t-SNE Dimension Reduction</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: t-SNE Dimension Reduction</p></td></tr><tr><td style='padding: 15px;'><p>6. Feature Selection to Select the Most Relevant Predictors</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Feature Selection to Select the Most Relevant Predictors</p></td></tr><tr><td style='padding: 15px;'><p>7. Recursive Feature Elimination (RFE)</p><p style='margin: 0'>Dimension Reduction & Feature Selection for Machine Learning: Recursive Feature Elimination (RFE)</p></td></tr></tbody></table><br><h4>6. Supervised Learning: Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Concepts Behind Supervised Learning</p><p style='margin: 0'>Supervised Learning: Classification: Concepts Behind Supervised Learning</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Preparation for Supervised Learning</p><p style='margin: 0'>Supervised Learning: Classification: Data Preparation for Supervised Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Pointers on Evaluating the Accuracy of Classification Modelling</p><p style='margin: 0'>Supervised Learning: Classification: Pointers on Evaluating the Accuracy of Classification Modelling</p></td></tr><tr><td style='padding: 15px;'><p>4. Using Logistic Regression as a Classification Model</p><p style='margin: 0'>Supervised Learning: Classification: Using Logistic Regression as a Classification Model</p></td></tr><tr><td style='padding: 15px;'><p>5. kNN- Classification</p><p style='margin: 0'>Supervised Learning: Classification: kNN- Classification</p></td></tr><tr><td style='padding: 15px;'><p>6. Naive Bayes Classification</p><p style='margin: 0'>Supervised Learning: Classification: Naive Bayes Classification</p></td></tr><tr><td style='padding: 15px;'><p>7. Linear Discriminant Analysis</p><p style='margin: 0'>Supervised Learning: Classification: Linear Discriminant Analysis</p></td></tr><tr><td style='padding: 15px;'><p>8. SVM- Linear Classification</p><p style='margin: 0'>Supervised Learning: Classification: SVM- Linear Classification</p></td></tr><tr><td style='padding: 15px;'><p>9. Non-Linear SVM Classification</p><p style='margin: 0'>Supervised Learning: Classification: Non-Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>10. RF-Classification</p><p style='margin: 0'>Supervised Learning: Classification: RF-Classification</p></td></tr><tr><td style='padding: 15px;'><p>11. Gradient Boosting Machine (GBM)</p><p style='margin: 0'>Supervised Learning: Classification: Gradient Boosting Machine (GBM)</p></td></tr><tr><td style='padding: 15px;'><p>12. Voting Classifier</p><p style='margin: 0'>Supervised Learning: Classification: Voting Classifier</p></td></tr></tbody></table><br><h4>7. Neural Networks and Deep Learning Based Classification Techniques</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Perceptrons for Binary Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Perceptrons for Binary Classification</p></td></tr><tr><td style='padding: 15px;'><p>2. Artificial Neural Networks (ANN) for Binary Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Artificial Neural Networks (ANN) for Binary Classification</p></td></tr><tr><td style='padding: 15px;'><p>3. Multi-class Classification With MLP</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Multi-class Classification With MLP</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to H20</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Introduction to H20</p></td></tr><tr><td style='padding: 15px;'><p>5. Use H20 for Deep Learning Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: Use H20 for Deep Learning Classification</p></td></tr><tr><td style='padding: 15px;'><p>6. H20 Deep Learning for Classification</p><p style='margin: 0'>Neural Networks and Deep Learning Based Classification Techniques: H20 Deep Learning for Classification</p></td></tr></tbody></table>

Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis

Clustering and Classification with Machine Learning in Python

"Start your career as a Certified Python Programmer and earn up to £70,000 per Year."
Are you looking to make your mark in the ever-evolving world of Data Analyst? The demand for Python programmers has never been higher. Just in the UK, job postings for Analysts / Python programmers surged by an astounding 70% in 2022. This exponential growth trend signifies the soaring need for skilled Python programmers. Amid this vibrant job market, our Python Programmer online Training is your golden ticket to a prosperous career in programming or Data Analysis. Regardless of your level - beginner or expert - we ensure a comprehensive learning experience that will enhance your current skills and prepare you for the dynamic tech industry.
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Our mission is simple - to be your trusted partner every step of the way, from training to employment.
In addition to teaching you the technical skills you need, we will also provide you with career mentoring and support. We will help you build your resume, prepare for interviews, and land your dream job. We also have partnerships with many companies that are hiring Programmers, so we can help you get your foot in the door.
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Courses we will provide once you enrol in the program:
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Course 07: Business and Data Analytics for Beginners
Course 08: Data Analytics with Tableau
Course 09: Quick Data Science Approach from Scratch
Course 10: Learn MySQL from Scratch for Data Science and Analytics
Course 11: SQL for Data Science, Data Analytics and Data Visualization
Course 12: Statistics & Probability for Data Science & Machine Learning
Course 13: Basic Google Data Studio
Course 14: Machine Learning with Python Course
Course 15: Python Programming Bible | Networking, GUI, Email, XML, CGI
Course 16: Web Scraping and Mapping Dam Levels in Python and Leaflet
Course 17: Coding Essentials - Javascript, ASP. Net, C#
Course 19: Master JavaScript with Data Visualization
How Can We Offer Job Guarantees?
HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.
The Python Certification - IT Training is completed in 9 easy steps:
Step 1: Enrol in the Programme
Begin your exciting journey with us by enrolling in the Python Certification Training program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.
After you enrol in the Program, you will get lifetime access to 19 Premium Courses related to Python Programming. These courses will teach you the knowledge and skills required to become a successful Python Prgrammer. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!
Step 2: Initial One-On-One Counselling Session
Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.
Step 3 - Certification upon Course Completion
After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.
Step 4: CV Revamping
Our team of professionals will build you a compelling CV and LinkedIn profile. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the programming/development industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently.
Step 5: Building Network and Submitting CV
We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many high-profile individuals and companies through your LinkedIn profile.
Step 6: Interview Preparation
With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.
Step 7: Securing Job Interviews
Leveraging our partnerships with leading companies, we'll secure job interviews for you. We'll ensure you get the opportunity to showcase your skills to potential employers and get the dream job you want.
Step 8: Post-Interview Support
Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.
Step 9: Celebrate Your New Job!
Once you've secured your dream job in Python Programming, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.
We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Python Programming begins with us.
 CPD 
100 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
This Python Certification Course is suitable for:
Beginners with no prior programming experience who are looking to enter the world of coding.
Intermediate programmers who wish to expand their skills and explore Python's extensive usage.
IT professionals seeking to diversify their skill sets and stay relevant in today's tech-driven market.
Data analysts and scientists who want to leverage Python's powerful libraries for their data processing needs.
Students and recent graduates looking to enhance their employability in the thriving tech industry.
Entrepreneurs who wish to understand and utilise Python to automate tasks and analyse data for their businesses.
Anyone with a keen interest in Python programming and a desire to delve into its applications in diverse fields like web development, data science, machine learning, and more.
 Requirements 
No experience required. Just enrol & start learning python.
 Career path 
Upon completion of our Program, the potential career paths include:
Python Developer: £30,000 - £90,000 annually
Data Scientist: £40,000 - £100,000 annually
Machine Learning Engineer: £50,000 - £110,000 annually
Data Analyst: £35,000 - £85,000 annually
Software Engineer: £35,000 - £95,000 annually
Back-end Developer: £40,000 - £95,000 annually
Web Developer: £30,000 -£85,000 annually
 Certificates 
CPD Accredited e-Certificate
 Digital certificate - Included 
CPD Accredited Framed (Hardcopy) Certificate
 Hard copy certificate - Included 
Enrolment Letter
 Digital certificate - Included 
QLS Endorsed Hard Copy Certificate
 Hard copy certificate - Included 
Student ID Card
 Digital certificate - Included 

Academy for Health and Fitness

Programming Fundamentals

Career Development

IT Certifications

Become an IT professional with our Python Certification - IT Job Ready Program! Gain comprehensive skills in Python programming, data analysis, and automation. We offer a money-back guarantee if you don't secure a job within six months of completion. Enroll now and jumpstart your tech career!


Ready to land your dream job in tech? Our Python Certification - IT Job Ready Program equips you with essential Python programming, data analysis, and automation skills. Plus, we offer a money-back guarantee if you don't get hired within six months of completing the course. Don't miss out – enroll today and take the first step towards a successful IT career! 💻📊 #PythonCertification #TechTraining #CareerReady #MoneyBackGuarantee

Python Certification - IT Job Ready Program with Money Back Guarantee

<strong>Learn Python Programming the Easy Way, Complete with Examples, Quizzes, Exercises and more. Learn Python 2 and Python 3.</strong>
        <p>This course will teach you Python in a practical manner, complete with examples, quizzes, exercises, and more. You will learn when to use Python 2 and Python 3; both versions are covered in the course and you will learn to program in both. You'll learn how to take tedious and repetitious tasks and turn them into programs that will save you time and simplify your life on Linux, UNIX, or MAC systems. You will learn how to prepare your computer for programming in Python; how to work with various data types including strings, lists, tuples, dictionaries, Booleans; how to perform mathematical operations using Python, and more.</p>
        <h2>What You Will Learn</h2><p>When to use Python 2 and when to use Python 3.<br>How to install Python on Windows, Mac, and Linux.<br>How to prepare your computer for programming in Python.<br>The various ways to run a Python program on Windows, Mac, and Linux.<br>Suggested text editors and Integrated Development Environments to use when coding in Python.<br>How to work with various data types including strings, lists, tuples, dictionaries, Booleans, and more.<br>What variables are and when to use them.<br>How to perform mathematical operations using Python.<br>How to capture input from a user.<br>Ways to control the flow of your programs.<br>The importance of white space in Python.<br>How to organize your Python programs: learn what goes where.<br>What modules are, when you should use them, and how to create your own.<br>How to define and use functions.<br>Important built-in Python functions that you ll use often.<br>How to read from and write to files.<br>The difference between binary and text files.<br>Various ways of getting help, including finding Python documentation.<br></p>
        <h2>Audience</h2><p>This course targets anyone interested in Python programming, Python scripting, or computer programming in general; those who want to become a highly paid Python developer; and those who want to open up doors in their IT career by learning one of the world's most popular and in-demand programming languages.</p>
        <h2>Approach</h2><p>A complete course packed with step-by-step instructions, practice exercises with solutions, and quizzes. This course is clearly divided into small parts to help you understand each part individually and learn at your own pace. Once you've completed this course you'll know how to write programs that will run on the Linux, Mac, and Windows operating systems. You can even take what you've learned and apply it to web applications. </p>
        <h2>Key Features</h2><p>How to install Python on Windows, Mac, and Linux. * A practical tutorial designed for Python beginners. * </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/python-for-beginners-learn-python-programming-python-3-</p>
        <h2>About the Author</h2><h3>Jason Cannon</h3><p>Jason Cannon is a professional system administrator, consultant, and author. He started his career as a Unix and Linux System engineer in 1999. Since that time, he has utilized his Linux skills at companies such as Xerox, UPS, Hewlett-Packard, and Amazon.com. Additionally, he has acted as a technical consultant and independent contractor for small businesses and Fortune 500 companies.

Jason has professional experience with CentOS, RedHat Enterprise Linux, SUSE Linux Enterprise Server, and Ubuntu. He has used several Linux distributions on personal projects including Debian, Slackware, CrunchBang, and others. In addition to Linux, Jason has experience supporting proprietary Unix operating systems including AIX, HP-UX, and Solaris. He enjoys teaching others how to use and exploit the power of the Linux operating system.</p>
        <h2>Course Outline</h2><h4>1. Course Overview and Downloads</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Overview</p><p style='margin: 0'>Course Overview and Downloads: Course Overview</p></td></tr></tbody></table><br><h4>2. Python Setup</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Python 2 vs Python 3</p><p style='margin: 0'>Python Setup: Python 2 vs Python 3</p></td></tr><tr><td style='padding: 15px;'><p>2. Installing Python on Windows</p><p style='margin: 0'>Python Setup: Installing Python on Windows</p></td></tr><tr><td style='padding: 15px;'><p>3. Installing Python on Mac</p><p style='margin: 0'>Python Setup: Installing Python on Mac</p></td></tr><tr><td style='padding: 15px;'><p>4. Installing Python on Linux</p><p style='margin: 0'>Python Setup: Installing Python on Linux</p></td></tr><tr><td style='padding: 15px;'><p>5. Writing Python Programs</p><p style='margin: 0'>Python Setup: Writing Python Programs</p></td></tr><tr><td style='padding: 15px;'><p>6. Running Python Programs</p><p style='margin: 0'>Python Setup: Running Python Programs</p></td></tr></tbody></table><br><h4>3. Strings and Variables</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Strings and Variables: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Variables and Strings</p><p style='margin: 0'>Strings and Variables: Variables and Strings</p></td></tr><tr><td style='padding: 15px;'><p>3. Functions and Printing</p><p style='margin: 0'>Strings and Variables: Functions and Printing</p></td></tr><tr><td style='padding: 15px;'><p>4. String Methods</p><p style='margin: 0'>Strings and Variables: String Methods</p></td></tr><tr><td style='padding: 15px;'><p>5. String Concatenation</p><p style='margin: 0'>Strings and Variables: String Concatenation</p></td></tr><tr><td style='padding: 15px;'><p>6. Formatting Strings</p><p style='margin: 0'>Strings and Variables: Formatting Strings</p></td></tr><tr><td style='padding: 15px;'><p>7. Section Summary</p><p style='margin: 0'>Strings and Variables: Section Summary</p></td></tr><tr><td style='padding: 15px;'><p>8. Solutions to the Practice Exercises - #1 - Video</p><p style='margin: 0'>Strings and Variables: Solutions to the Practice Exercises - #1 - Video</p></td></tr><tr><td style='padding: 15px;'><p>9. Solutions to the Practice Exercises - #2 - Video</p><p style='margin: 0'>Strings and Variables: Solutions to the Practice Exercises - #2 - Video</p></td></tr><tr><td style='padding: 15px;'><p>10. Solutions to the Practice Exercises - #3 - Video</p><p style='margin: 0'>Strings and Variables: Solutions to the Practice Exercises - #3 - Video</p></td></tr></tbody></table><br><h4>4. Numbers and Math</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Numbers and Math: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Numbers, Numeric Operations, and Numeric Functions</p><p style='margin: 0'>Numbers and Math: Numbers, Numeric Operations, and Numeric Functions</p></td></tr><tr><td style='padding: 15px;'><p>3. Comments</p><p style='margin: 0'>Numbers and Math: Comments</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Summary</p><p style='margin: 0'>Numbers and Math: Section Summary</p></td></tr></tbody></table><br><h4>5. Booleans and Conditionals</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Booleans and Conditionals: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Booleans</p><p style='margin: 0'>Booleans and Conditionals: Booleans</p></td></tr><tr><td style='padding: 15px;'><p>3. Conditionals</p><p style='margin: 0'>Booleans and Conditionals: Conditionals</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Summary</p><p style='margin: 0'>Booleans and Conditionals: Section Summary</p></td></tr></tbody></table><br><h4>6. Functions</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Functions: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Functions, Part I</p><p style='margin: 0'>Functions: Functions, Part I</p></td></tr><tr><td style='padding: 15px;'><p>3. Functions, Part II</p><p style='margin: 0'>Functions: Functions, Part II</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Summary</p><p style='margin: 0'>Functions: Section Summary</p></td></tr></tbody></table><br><h4>7. Lists</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Lists: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Lists</p><p style='margin: 0'>Lists: Lists</p></td></tr><tr><td style='padding: 15px;'><p>3. Slices</p><p style='margin: 0'>Lists: Slices</p></td></tr><tr><td style='padding: 15px;'><p>4. Exception Handling</p><p style='margin: 0'>Lists: Exception Handling</p></td></tr><tr><td style='padding: 15px;'><p>5. Loops</p><p style='margin: 0'>Lists: Loops</p></td></tr><tr><td style='padding: 15px;'><p>6. Sorting and Ranges</p><p style='margin: 0'>Lists: Sorting and Ranges</p></td></tr><tr><td style='padding: 15px;'><p>7. Section Summary</p><p style='margin: 0'>Lists: Section Summary</p></td></tr></tbody></table><br><h4>8. Dictionaries</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Dictionaries: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Dictionaries - Part I</p><p style='margin: 0'>Dictionaries: Dictionaries - Part I</p></td></tr><tr><td style='padding: 15px;'><p>3. Dictionaries - Part II</p><p style='margin: 0'>Dictionaries: Dictionaries - Part II</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Summary</p><p style='margin: 0'>Dictionaries: Section Summary</p></td></tr></tbody></table><br><h4>9. Tuples</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Tuples: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Tuples</p><p style='margin: 0'>Tuples: Tuples</p></td></tr><tr><td style='padding: 15px;'><p>3. Section Summary</p><p style='margin: 0'>Tuples: Section Summary</p></td></tr></tbody></table><br><h4>10. Files</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Files: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Files, Part I</p><p style='margin: 0'>Files: Files, Part I</p></td></tr><tr><td style='padding: 15px;'><p>3. Files, Part II</p><p style='margin: 0'>Files: Files, Part II</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Summary</p><p style='margin: 0'>Files: Section Summary</p></td></tr></tbody></table><br><h4>11. Modules</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Modules: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Modules, Part I</p><p style='margin: 0'>Modules: Modules, Part I</p></td></tr><tr><td style='padding: 15px;'><p>3. Modules, Part II</p><p style='margin: 0'>Modules: Modules, Part II</p></td></tr><tr><td style='padding: 15px;'><p>4. Section Overview</p><p style='margin: 0'>Modules: Section Overview</p></td></tr></tbody></table>

Learn Python Programming the Easy Way, Complete with Examples, Quizzes, Exercises and more. Learn Python 2 and Python 3.

Python for Beginners: Learn Python Programming (Python 3)

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Nexus Human

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

DP-100T01 Designing and Implementing a Data Science Solution on Azure

<p><strong>Start your career in Data Science and earn up to £90,000 per month.</strong></p><p>Are you eager to dive into the high-speed world of Data Science powered by Python? In the UK alone, Data Scientist job postings witnessed a dramatic increase of 67% in 2022, emphasising the burgeoning demand for proficient Python programmers. Amid such a dynamic job market, our online course - Data Science with Python, serves as your stepping-stone to a universe of opportunities. Whether you're taking your first step into the realm of Data Science or aiming to augment your existing skills, our program offers peerless support, ensuring you're industry-ready by the time you complete our course.</p><p><strong>Our mission is simple</strong> - to be your trusted partner every step of the way, from training to employment.</p><p>In addition to teaching you the technical skills you need, we will also provide you with <strong>career mentoring and support</strong>. We will help you build your resume, prepare for interviews, and land your <strong>dream job</strong>. We also have<strong> partnerships with many companies</strong> that are hiring Data <strong>Scientists</strong>, so we can help you get your foot in the door.</p><p>If you are not happy with our service, we also offer a <strong>100% money-back guarantee</strong>. So what are you waiting for? Enrol in our Data Scientist with Python Training Program today and start your journey to becoming a successful Data Scientist!</p><p>If you have any questions, you can <strong>contact us</strong>. We will be happy to provide you with all the information you need.</p><p><strong>Why Choose Us?</strong></p><p>So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Scientist with Python:</p><ul><li><strong>Personalised Guidance:</strong> We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Python experience, we will guide you towards honing your skills and developing a strong foundation in both Data Science and Python.<br /><br /></li><li><strong>One-On-One Consultation Sessions with Industry Experts:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Science field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.<br /><br /></li><li><strong>Extensive Job Opportunities:</strong> We have established partnerships with numerous companies actively seeking Data Scientists. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere.<br /><br /></li><li><strong>Interview Preparation:</strong> No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.<br /><br /></li><li><strong>Money-Back Guarantee:</strong> Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a <strong>100% money-back guarantee. </strong>If, for any reason, you're not happy with our services, we'll refund your investment, no questions asked. We believe in the value we provide and want you to feel completely satisfied with your decision to join us.<br /><br /></li><li><strong>Continuous Career Support: </strong>Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance.</li></ul><p><strong>Here are the courses we will provide once you enrol in the program:</strong></p><ul><li>Course 01: Business and Data Analytics for Beginners</li><li>Course 02: Quick Data Science Approach from Scratch</li><li>Course 03: Learn MySQL from Scratch for Data Science and Analytics</li><li>Course 04: SQL for Data Science, Data Analytics and Data Visualization</li><li>Course 05: Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li>Course 06: R Programming for Data Science</li><li>Course 07: Python Data Science with Numpy, Pandas and Matplotlib</li><li>Course 08: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li>Course 09: 2021 Data Science &amp; Machine Learning with R from A-Z</li><li>Course 10: Python Programming from Scratch with My SQL Database</li><li>Course 11: Level 2 Python Course</li><li>Course 12: Machine Learning for Predictive Maps in Python and Leaflet</li><li>Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li>Course 14: Python for Spatial Analysis in ArcGIS</li><li>Course 15: Ultimate Python Training for Beginners</li><li>Course 16: PyScript Fundamentals Training</li></ul><p><strong>How Can We Offer Job Guarantees?</strong></p><p>HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.</p> <p><strong>The </strong><strong>Data Scientist with Python </strong><strong>Program is completed in 9 easy steps:<br /><br /></strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the Data Science with Python Training program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.</p><p>After you enrol in the Program, you will get lifetime access to 16 premium courses related to Data Science with Python. These courses will teach you the knowledge and skills required to become a successful Data Scientist. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!</p><p><strong>Step 2: Initial One-On-One Counselling Session</strong></p><p>Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.<br /><br /></p><p><strong>Step 3 - </strong><strong>Certification upon Course Completion</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.<br /><br /></p><p><strong>Step 4: </strong><strong>CV Revamping</strong></p><p>Our team of professionals will build you a compelling CV and <strong>LinkedIn profile</strong>. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Data Science with Python industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. <br /><br /></p><p><strong>Step 5: Building Network and Submitting CV</strong></p><p>We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many<strong> high-profile individuals and companies</strong> through your LinkedIn profile.<br /><br /></p><p><strong>Step 6: Interview Preparation</strong></p><p>With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.<br /><br /></p><p><strong>Step 7: Securing Job Interviews</strong></p><p>Leveraging our partnerships with leading companies, <strong>we'll secure job interviews for you</strong>. We'll ensure you get the opportunity to showcase your skills to potential employers and <strong>get the dream job you want</strong>.<br /><br /></p><p><strong>Step 8: Post-Interview Support</strong></p><p>Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.<br /><br /></p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in Data Science with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.</p><p>We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Science with Python begins with us.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        100 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Beginners with an interest in Data Science and Python programming.</li><li>Programmers and Software Engineers looking to transition into Data Science.</li><li>Business Analysts and Project Managers aiming to leverage Data Science in their domains.</li><li>Researchers and Academics who wish to utilise Python for complex data analysis.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Scientist: £60,000 - £90,000</li><li>Machine Learning Engineer: £65,000 - £100,000</li><li>Data Analyst: £40,000 - £60,000</li><li>Business Intelligence Analyst: £45,000 - £70,000</li><li>Data Engineer: £50,000 - £80,000</li><li>Big Data Architect: £70,000 - £120,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>QLS Endorsed Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Start your career in Data Science and earn up to £90,000 per month.
Are you eager to dive into the high-speed world of Data Science powered by Python? In the UK alone, Data Scientist job postings witnessed a dramatic increase of 67% in 2022, emphasising the burgeoning demand for proficient Python programmers. Amid such a dynamic job market, our online course - Data Science with Python, serves as your stepping-stone to a universe of opportunities. Whether you're taking your first step into the realm of Data Science or aiming to augment your existing skills, our program offers peerless support, ensuring you're industry-ready by the time you complete our course.
Our mission is simple - to be your trusted partner every step of the way, from training to employment.
In addition to teaching you the technical skills you need, we will also provide you with career mentoring and support. We will help you build your resume, prepare for interviews, and land your dream job. We also have partnerships with many companies that are hiring Data Scientists, so we can help you get your foot in the door.
If you are not happy with our service, we also offer a 100% money-back guarantee. So what are you waiting for? Enrol in our Data Scientist with Python Training Program today and start your journey to becoming a successful Data Scientist!
If you have any questions, you can contact us. We will be happy to provide you with all the information you need.
Why Choose Us?
So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Data Scientist with Python:
Personalised Guidance: We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Python experience, we will guide you towards honing your skills and developing a strong foundation in both Data Science and Python.
One-On-One Consultation Sessions with Industry Experts: Gain invaluable insights and guidance from seasoned professionals who have thrived in the Data Science field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.
Extensive Job Opportunities: We have established partnerships with numerous companies actively seeking Data Scientists. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere.
Interview Preparation: No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.
Money-Back Guarantee: Your satisfaction is our top priority. We are confident in the quality of our training and support, which is why we offer a 100% money-back guarantee. If, for any reason, you're not happy with our services, we'll refund your investment, no questions asked. We believe in the value we provide and want you to feel completely satisfied with your decision to join us.
Continuous Career Support: Our commitment doesn't end when you secure a job. We'll be there for you throughout your career journey, offering continued support and guidance.
Here are the courses we will provide once you enrol in the program:
Course 01: Business and Data Analytics for Beginners
Course 02: Quick Data Science Approach from Scratch
Course 03: Learn MySQL from Scratch for Data Science and Analytics
Course 04: SQL for Data Science, Data Analytics and Data Visualization
Course 05: Statistics & Probability for Data Science & Machine Learning
Course 06: R Programming for Data Science
Course 07: Python Data Science with Numpy, Pandas and Matplotlib
Course 08: Complete Python Machine Learning & Data Science Fundamentals
Course 09: 2021 Data Science & Machine Learning with R from A-Z
Course 10: Python Programming from Scratch with My SQL Database
Course 11: Level 2 Python Course
Course 12: Machine Learning for Predictive Maps in Python and Leaflet
Course 13: Python Programming Bible | Networking, GUI, Email, XML, CGI
Course 14: Python for Spatial Analysis in ArcGIS
Course 15: Ultimate Python Training for Beginners
Course 16: PyScript Fundamentals Training
How Can We Offer Job Guarantees?
HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.
The Data Scientist with Python Program is completed in 9 easy steps:


Step 1: Enrol in the Programme
Begin your exciting journey with us by enrolling in the Data Science with Python Training program. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.
After you enrol in the Program, you will get lifetime access to 16 premium courses related to Data Science with Python. These courses will teach you the knowledge and skills required to become a successful Data Scientist. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!
Step 2: Initial One-On-One Counselling Session
Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.


Step 3 - Certification upon Course Completion
After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.


Step 4: CV Revamping
Our team of professionals will build you a compelling CV and LinkedIn profile. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the Data Science with Python industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently. 


Step 5: Building Network and Submitting CV
We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many high-profile individuals and companies through your LinkedIn profile.


Step 6: Interview Preparation
With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.


Step 7: Securing Job Interviews
Leveraging our partnerships with leading companies, we'll secure job interviews for you. We'll ensure you get the opportunity to showcase your skills to potential employers and get the dream job you want.


Step 8: Post-Interview Support
Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.


Step 9: Celebrate Your New Job!
Once you've secured your dream job in Data Science with Python, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.
We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Data Science with Python begins with us.
 CPD 
100 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
Beginners with an interest in Data Science and Python programming.
Programmers and Software Engineers looking to transition into Data Science.
Business Analysts and Project Managers aiming to leverage Data Science in their domains.
Researchers and Academics who wish to utilise Python for complex data analysis.
 Requirements 
No experience required. Just enrol & start learning.
 Career path 
Data Scientist: £60,000 - £90,000
Machine Learning Engineer: £65,000 - £100,000
Data Analyst: £40,000 - £60,000
Business Intelligence Analyst: £45,000 - £70,000
Data Engineer: £50,000 - £80,000
Big Data Architect: £70,000 - £120,000
 Certificates 
CPD Accredited e-Certificate
 Digital certificate - Included 
CPD Accredited Framed (Hardcopy) Certificate
 Hard copy certificate - Included 
Enrolment Letter
 Digital certificate - Included 
QLS Endorsed Hard Copy Certificate
 Hard copy certificate - Included 
Student ID Card
 Digital certificate - Included 

Big Data Analytics

Data Skills

Data Science with Python - IT Job Ready Program with Career Support & Money Back Guarantee

<strong>You will master the fundamentals and more advanced principles of Python programming in this beginner's tutorial. This Python course covers every Python fundamental, including installation and more complex material such as Python data science.</strong>
        <p>'Python is the future of artificial intelligence and machine learning.'-Adrian Rosebrock, author.

Python is the programming language most loved by developers, data scientists, software engineers, and even hackers.

This Python basic to advanced course covers the fundamental concepts of Python programming and ways to apply this to real-world applications. The course modules, lesson-end quizzes, and lesson-end projects collectively cover the training course topics such as data operations, strings, file I/O, exception handling, error handling, conditional statements, CGI programming, shell scripting, web scraping, and Django.

It also covers Logging Framework, Unittest Framework, Socket Programming, Tornado Framework, and Networking. You will also build a Python chat server.

By the end of this course, you will have the skills to create and send robust Python applications using off-the-rack libraries confidently.

The code bundle for this course is available at: https://github.com/PacktPublishing/Python-Programming</p>
        <h2>What You Will Learn</h2><p>Learn the basics of Python<br>Learn networking and database access using Python and CGI programming<br>Understand the Python debugger<br>Understand web scraping using Beautiful Soup<br>Learn about Python Unittest<br>Build a Python chat server</p>
        <h2>Audience</h2><p>This course is for new Python users looking to make the best of this powerful programming language-college freshers, software developers, programming enthusiasts, and technical leads. Python is widely used in artificial intelligence, so if you are planning to start your career in AI/machine learning then you need to have an understanding of Python as a prerequisite.</p>
        <h2>Approach</h2><p>This course comprises self-paced videos, demos, lesson-end quizzes, and a competitive course-end project. This will help you increase your in-depth understanding of Python's design, memory models, object-arranged highlights, and information, and be industry-prepared.</p>
        <h2>Key Features</h2><p>Go from a beginner level to a confident level of Python programming * Increase your in-depth understanding of Python's design, memory models, and more * Work on a competitive project at the end of the course</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/python-programming</p>
        <h2>About the Author</h2><h3>Learnkart Technology Private Limited</h3><p>Learnkart understands the importance of upskilling and its impact on you. They pride themselves in creating specialized e-learning courses that will not only prepare you for certification exams but also help you gain hands-on knowledge for real-world applications. Thousands of students have developed their skills with Learnkart through a variety of courses, such as PgMP, Risk Management Professional (RMP), speech recognition, Python, Amazon Web Services (AWS), and more. Learnkart regularly adds courses to its portfolio to lend more value to your learning experience.</p>
        <h2>Course Outline</h2><h4>1. Course Introduction</h4><p></p><br><h4>2. Python Basics</h4><p></p><br><h4>3. Data Operations</h4><p></p><br><h4>4. Conditional Statements and Functions</h4><p></p><br><h4>5. Error Handling and File Operations</h4><p></p><br><h4>6. Shell Scripting and Django</h4><p></p><br><h4>7. Course End Project</h4><p></p>

You will master the fundamentals and more advanced principles of Python programming in this beginner's tutorial. This Python course covers every Python fundamental, including installation and more complex material such as Python data science.

Python Programming

Master Python 3.10 from the experts with this course and demonstrate programming wizardry in Python. Go from a geek to a wizard in no time with this well-structured video course and write Python code like a pro. Learn about the often-forgotten basics and special features of programming and Python in general.

The Ultimate Guide to Python Programming with Python 3.10

<strong>This course offers you the exact skills and principles you need to deliver a project using Python along with the tools you can use to create solid Python code. Master the language constructs, tools, and terminology with this carefully designed course for beginners.</strong>
        <p>Python is one of the most popular computer programming languages and this hands-on course for beginners will make it easy for someone who wants to get started with learning Python.

This course takes you through the introduction to the course and the learning objectives. You will learn to get started with Replit and write your first Python program. You will learn to store, process, and compare information in Python as well as learn to tidy messy data. You will learn about variables, strings, dictionaries, indexing, and slicing in Python. Further, you will understand loops in Python along with break, continue, and else functions.

Moving ahead, you will understand error handling in Python, get to know the try-and-catch block, and learn to add resources to Replit. You will also understand function parameters and explore the difference between local and global variables, and positional and keyword parameters. You will learn to work with JSON and clean the data along with various numeric functions such as min, max, and sum.

By the end of the course, you will gain a solid foundation in Python programming and be able to write high-quality code in Python.

All resources and code files are placed here: https://github.com/PacktPublishing/Python-Bootcamp-in-a-day---Python-Programming-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Master Python programming fundamentals<br>Learn how to install Python to get up and running in no time<br>Learn how to process data to create Olympics Medals Tables<br>Solve multiple specific problems for a customer in the real world<br>Learn all about objects and variables in Python<br>Understand functions and function parameters in Python</p>
        <h2>Audience</h2><p>Whether you are a software engineer, software developer, computer programmer, web developer, transitioning into a new role, or simply someone who wants to understand what makes Python tick, this is the place to start. If you are preparing for a Python certification or job role, this course is for you.</p>
        <h2>Approach</h2><p>This course is video based course with no supporting document necessary. It takes you through the fundamentals of Python and teaches you how to use it with a simple tool so you can master the fundamentals in a day.</p>
        <h2>Key Features</h2><p>Be fluent in writing Python code and working with all the fundamentals of the language such as a list, string, function, dictionary, array, set, and much more * Learn to use Python's data analysis capabilities to analyze data from a real-world data source * A complete overview of the basics with exercises to test your knowledge of storing, processing, and storing information</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Bootcamp-in-a-day---Python-Programming-for-Beginners</p>
        <h2>About the Author</h2><h3>Paul Ashun</h3><p>Paul Ashun is the CEO, MD, and chief consultant at Pashun Consulting Ltd., specializing in Scrum coaching and leadership within major global organizations. They are the authors of over 10 books on Scrum such as The Power of Scrum in the Real World, Confessions of a Scrum Master as well as the upcoming Agile User Storybook.

He started as a software developer and over ten years later, he became an Agile portfolio manager. He is a certified Scrum Master and a PMO manager. He has led projects for the BBC, General Electric, Oracle, BSkyB, HiT Entertainment, and Razorfish. He has been coaching product owners and business analysts in international blue-chip companies, dating back to 1999, in Agile and Scrum practices such as writing user stories.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video is an introduction the course and what you can expect from the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Learning Objectives - Introduction</p><p style='margin: 0'>This video explains the learning objectives of the course.</p></td></tr><tr><td style='padding: 15px;'><p>3. What Is Python?</p><p style='margin: 0'>This video explains what Python is and the need for Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. The Example</p><p style='margin: 0'>This video explains an example of a software engineer working in a media company to support the development team.</p></td></tr></tbody></table><br><h4>2. Getting Set Up</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Getting Set Up</p><p style='margin: 0'>This video explains the learning objectives of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. How to Get Started with Replit</p><p style='margin: 0'>This video explains how to get started with Replit.</p></td></tr><tr><td style='padding: 15px;'><p>3. Writing Your First Python Program-Hello World</p><p style='margin: 0'>This video explains writing your first Python program-Hello World.</p></td></tr></tbody></table><br><h4>3. Storing Information</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Storing Information</p><p style='margin: 0'>This video explains the learning objectives of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Objects and Variables</p><p style='margin: 0'>This video explains objects and variables in Python using an example of Olympic results.</p></td></tr><tr><td style='padding: 15px;'><p>3. Variable Names</p><p style='margin: 0'>This video explains variable names in Python and the rules to use them.</p></td></tr><tr><td style='padding: 15px;'><p>4. Numbers</p><p style='margin: 0'>This video explains the different types of numbers in Python.</p></td></tr><tr><td style='padding: 15px;'><p>5. Strings</p><p style='margin: 0'>This video explains strings in Python.</p></td></tr><tr><td style='padding: 15px;'><p>6. Showing Output When Running Python</p><p style='margin: 0'>This video explains showing output when running Python.</p></td></tr></tbody></table><br><h4>4. Processing Information</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Processing Information</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Processing Information</p><p style='margin: 0'>This video explains processing information in Python.</p></td></tr><tr><td style='padding: 15px;'><p>3. Indexing</p><p style='margin: 0'>This video explains indexing in Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. Slices</p><p style='margin: 0'>This video explains slices in Python.</p></td></tr><tr><td style='padding: 15px;'><p>5. String Functions</p><p style='margin: 0'>This video explains string functions in Python.</p></td></tr><tr><td style='padding: 15px;'><p>6. The 'input' Function</p><p style='margin: 0'>This video explains the input function in Python.</p></td></tr><tr><td style='padding: 15px;'><p>7. More String Operations</p><p style='margin: 0'>This video explains more string operations in Python.</p></td></tr><tr><td style='padding: 15px;'><p>8. Converting Between Different Types</p><p style='margin: 0'>This video explains converting between different types.</p></td></tr></tbody></table><br><h4>5. Comparing Information</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Comparing Information</p><p style='margin: 0'>This video explains the learning objectives of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Comparing Information</p><p style='margin: 0'>This video explains comparing information in Python.</p></td></tr></tbody></table><br><h4>6. Tidying Messy Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Tidying Messy Data</p><p style='margin: 0'>This video explains the learning objectives of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Tidying Messy Data</p><p style='margin: 0'>This video explains how to tidy messy data.</p></td></tr></tbody></table><br><h4>7. Storing Multiple Things</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Storing Multiple Things</p><p style='margin: 0'>This video explains the learning objectives of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Storing Multiple Things</p><p style='margin: 0'>This video explains how to store multiple things.</p></td></tr><tr><td style='padding: 15px;'><p>3. Introducing Python Lists</p><p style='margin: 0'>This video explains lists in Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. Adding Items to a List</p><p style='margin: 0'>This video explains how to add items to a list.</p></td></tr><tr><td style='padding: 15px;'><p>5. Indexing and Slicing Lists</p><p style='margin: 0'>This video explains indexing and slicing lists.</p></td></tr><tr><td style='padding: 15px;'><p>6. Deleting Items in a List</p><p style='margin: 0'>This video explains how to delete items in a list.</p></td></tr><tr><td style='padding: 15px;'><p>7. Tuples</p><p style='margin: 0'>This video explains tuples in Python.</p></td></tr><tr><td style='padding: 15px;'><p>8. Long Lines in Python</p><p style='margin: 0'>This video explains long lines in Python.</p></td></tr><tr><td style='padding: 15px;'><p>9. Dictionaries</p><p style='margin: 0'>This video explains dictionaries in Python.</p></td></tr><tr><td style='padding: 15px;'><p>10. List Functions - More Ways to Work with Lists</p><p style='margin: 0'>This video explains list functions.</p></td></tr><tr><td style='padding: 15px;'><p>11. Sets</p><p style='margin: 0'>This video explains sets in Python.</p></td></tr></tbody></table><br><h4>8. Processing Collections</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Processing Collections</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. A Simple 'for' Loop</p><p style='margin: 0'>This video explains a simple for loop.</p></td></tr><tr><td style='padding: 15px;'><p>3. More Complex 'for' loops - 'break', 'continue', and 'else'</p><p style='margin: 0'>This video explains more complex 'for' loops.</p></td></tr><tr><td style='padding: 15px;'><p>4. The 'range' Function - How to Create a List of Numbers</p><p style='margin: 0'>This video explains how to create a list of numbers.</p></td></tr><tr><td style='padding: 15px;'><p>5. The 'while' Loop - Do Something for a While</p><p style='margin: 0'>This video explains the while loop.</p></td></tr></tbody></table><br><h4>9. Robust Programs - Handling Errors</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Robust Programs - Handling Errors</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Robust Programs - Handling Errors</p><p style='margin: 0'>This video explains error handling in Python.</p></td></tr><tr><td style='padding: 15px;'><p>3. Look Before You Leap</p><p style='margin: 0'>This video explains one way of making the code more robust, which is called 'look before you leap'.</p></td></tr><tr><td style='padding: 15px;'><p>4. The Try and Except Block</p><p style='margin: 0'>This video explains the try and except block in Python.</p></td></tr><tr><td style='padding: 15px;'><p>5. The Catch-All Block</p><p style='margin: 0'>This video explains the catch-all block.</p></td></tr><tr><td style='padding: 15px;'><p>6. Adding Resources to Replit</p><p style='margin: 0'>This video explains how to add resources to Replit.</p></td></tr><tr><td style='padding: 15px;'><p>7. The Finally Block</p><p style='margin: 0'>This video explains the finally block.</p></td></tr></tbody></table><br><h4>10. Saving the Results - Working with Files</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Saving the Results - Working with Files</p><p style='margin: 0'>This video explains the learning objectives of the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Adding Bad Data</p><p style='margin: 0'>This video explains adding bad data.</p></td></tr><tr><td style='padding: 15px;'><p>3. Saving the Results - Working with Files</p><p style='margin: 0'>This video explains saving the results and working with files.</p></td></tr><tr><td style='padding: 15px;'><p>4. Binary File Modes</p><p style='margin: 0'>This video explains binary file modes.</p></td></tr><tr><td style='padding: 15px;'><p>5. Text File Modes</p><p style='margin: 0'>This video explains text file modes.</p></td></tr><tr><td style='padding: 15px;'><p>6. The With Statement</p><p style='margin: 0'>This video explains the with statement.</p></td></tr></tbody></table><br><h4>11. Breaking Up Larger Programs, Part 1</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Breaking Up Larger Programs, Part 1</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. About Replit.config</p><p style='margin: 0'>This video explains replit.config.</p></td></tr><tr><td style='padding: 15px;'><p>3. Functions</p><p style='margin: 0'>This video explains functions in Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. Function Parameters</p><p style='margin: 0'>This video explains function parameters.</p></td></tr><tr><td style='padding: 15px;'><p>5. Local Versus Global Variables</p><p style='margin: 0'>This video explains local versus global variables.</p></td></tr><tr><td style='padding: 15px;'><p>6. Positional and Keyword Parameters</p><p style='margin: 0'>This video explains positional and keyword parameters.</p></td></tr><tr><td style='padding: 15px;'><p>7. Return Statement</p><p style='margin: 0'>This video explains the return statement.</p></td></tr><tr><td style='padding: 15px;'><p>8. Working With JSON</p><p style='margin: 0'>This video explains working with JSON.</p></td></tr><tr><td style='padding: 15px;'><p>9. Code Comments and Clear Code</p><p style='margin: 0'>This video explains code comments in Python.</p></td></tr></tbody></table><br><h4>12. Summarising and Presenting the Results</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Summarising and Presenting the Results</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Summarising and Presenting the Results Part 1</p><p style='margin: 0'>This video explains how to summarise the results using statistics.</p></td></tr><tr><td style='padding: 15px;'><p>3. Cleaning the Data</p><p style='margin: 0'>This video explains cleaning the data in Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. Summarising and Presenting the Results Part 2</p><p style='margin: 0'>This video explains the code in which we will get a sum of the scores of a country.</p></td></tr><tr><td style='padding: 15px;'><p>5. More Numeric Functions - Min, Max, Sum</p><p style='margin: 0'>This video explains min, max, and sum functions in Python.</p></td></tr><tr><td style='padding: 15px;'><p>6. Formatted Strings, f-strings</p><p style='margin: 0'>This video explains formatted strings and f-strings in Python.</p></td></tr></tbody></table><br><h4>13. Breaking Up Larger Programs, Part 2</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Breaking Up Larger Programs, Part 2</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Splitting Code into Modules</p><p style='margin: 0'>This video explains splitting code into modules.</p></td></tr><tr><td style='padding: 15px;'><p>3. Namespaces</p><p style='margin: 0'>This video explains namespaces in Python.</p></td></tr><tr><td style='padding: 15px;'><p>4. Using Our Modules</p><p style='margin: 0'>This video explains how to use modules in Python.</p></td></tr></tbody></table><br><h4>14. Getting Set Up on a PC or MAC</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives - Getting Set Up on a PC or MAC</p><p style='margin: 0'>This video explains the learning objectives of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Getting Set Up</p><p style='margin: 0'>This video explains how to set up Python on our systems.</p></td></tr><tr><td style='padding: 15px;'><p>3. Starting Python on Windows 10</p><p style='margin: 0'>This video explains how to start Python on Windows 10.</p></td></tr><tr><td style='padding: 15px;'><p>4. Starting a Terminal or Command Prompt on Windows</p><p style='margin: 0'>This video explains how to start a terminal or command prompt on Windows.</p></td></tr><tr><td style='padding: 15px;'><p>5. Installing Python on Windows</p><p style='margin: 0'>This video explains the installation of Python on Windows.</p></td></tr><tr><td style='padding: 15px;'><p>6. Installing Python on MacOS</p><p style='margin: 0'>This video explains the installation on Python on MacOS.</p></td></tr><tr><td style='padding: 15px;'><p>7. Quick Python Test - Running Python</p><p style='margin: 0'>This video explains how to run a Python script.</p></td></tr><tr><td style='padding: 15px;'><p>8. Python Scripts and IDLE</p><p style='margin: 0'>This video explains Python scripts and IDE.</p></td></tr><tr><td style='padding: 15px;'><p>9. Code Editors and IDEs</p><p style='margin: 0'>This video explains code editors and IDEs.</p></td></tr><tr><td style='padding: 15px;'><p>10. Running Python Scripts</p><p style='margin: 0'>This video explains how to run Python scripts.</p></td></tr></tbody></table>

This course offers you the exact skills and principles you need to deliver a project using Python along with the tools you can use to create solid Python code. Master the language constructs, tools, and terminology with this carefully designed course for beginners.

Python Bootcamp in a Day - Python Programming for Beginners.

Network

Networking

Cisco

This course is an excellent resource to learn network programming using Python. With the help of practical examples, you will learn how to automate networks with Telnet, Secure Shell (SSH), Paramiko, Netmiko, and Network Automation and Programmability Abstraction Layer with Multivendor support (NAPALM).

Python Network Programming for Network Engineers (Python 3)

<p><strong>Introducing Coding using Python</strong></p>
<p>This course introduces your child to a very advanced, yet easy to learn programming languages &ndash; Python. It&rsquo;s safe to say that many apps we use today have been built using Python &ndash; Instagram Web, Netflix, YouTube, to name a few! This course teaches the fundamentals of coding, and creates a solid foundation to solve more complex real-life problems.</p>

Riva Learning

Programming Languages

Computing

Choose this course if your child is new to Python or has done a few hours of Python before. Or, they have a good understanding of block-based platforms like Scratch, and would like to start exploring text-based programming languages.

Introducing coding using Python

General admission

<strong>Get to grip with the foundations of Python Programming language</strong>
        <p>The Python Code Camp will help you learn Python programming and the concepts associated with it. If you are a beginner, this is the best way to start your coding journey.
Starting with an introduction to the Python language and its importance, this course will take you through the concept of inputs and decisions, and enable you to explore topics like functions, dictionaries, lists, and tuples in detail. You'll then advance to learning about the various classes and objects along with the different file input and output modes. Every topic concludes with a campfire activity that includes exercises and practical examples to help you understand the concepts better.
By the end of the course, you will have developed a solid foundation for getting started with Python programming.

All the codes and supporting files for this course will be available at- https://github.com/PacktPublishing/Python-Code-Camp</p>
        <h2>What You Will Learn</h2><p>Get well-versed with the basics of Python Programming<br>Find out how you can simplify coding using functions in Python<br>Understand the implementation of classes and objects<br>Discover the different file input and output modes<br>Explore the different lists and tuples with the help of coding examples</p>
        <h2>Audience</h2><p>This course is for anyone who wants to learn the fundamentals of the Python programming language. Any beginner-level student will find this course useful.</p>
        <h2>Approach</h2><p>This hands-on course will help you with the concepts of Python programming using engaging exercises and coding examples.</p>
        <h2>Key Features</h2><p>Develop fundamental Python programming skills * Explore the different classes and objects in Python and learn to implement them * Understand how different functions work in Python</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/python-code-camp</p>
        <h2>About the Author</h2><h3>Mark Lassoff</h3><p>Framework Tech, founded by the visionary Mark Lassoff, specializes in teaching digitally accessible technical skills to people just getting started in their technical careers, including web development, digital design, and other career paths enabled by technology. Framework Tech has bridged the gap between entertainment and education, creating a unique, immersive learning experience that resonates with students.

Their instructors are more than just tech-savvy professionals; they are educators first and foremost, handpicked for their unique ability to translate complex technological concepts into digestible, engaging lessons. Framework Tech continues to evolve, constantly innovating to stay at the forefront of the online learning landscape.</p>
        <h2>Course Outline</h2><h4>1. Code Camp Day 001: Getting Started with Python</h4><p></p><br><h4>2.  Input and Decisions</h4><p></p><br><h4>3.  Lists and Tuples</h4><p></p><br><h4>4. Dictionaries</h4><p></p><br><h4>5. Functions</h4><p></p><br><h4>6. Classes and Objects</h4><p></p><br><h4>7. More Class and Objects</h4><p></p><br><h4>8. File Input and Output</h4><p></p>

Get to grip with the foundations of Python Programming language

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