Name: IT Security Job Guarantee Programme
Price: 1499 GBP
Availability: InStock

<p>Give a compliment to your career and take it to the next level. This<strong> Data Science </strong>will provide you with the essential knowledge to shine in your professional career. Whether you want to develop skills for your next job or elevate your skills for your next promotion, this Data Sciencebundle will help you stay ahead of the pack.</p><p>Throughout the Data Scienceprogramme, it stresses how to improve your competency as a person in your chosen field while also outlining essential career insights in the relevant job sector.</p><p>Along with this Data Science course, you will get <strong>10 premium courses, an original</strong><strong>Hardcopy</strong>, <strong>11 PDF Certificates (Main Course + Additional Courses) </strong>Student ID card as gifts.</p><p><strong>This Data Science Bundle Consists of the following Premium courses: </strong></p><ul><li><strong> Course 01: </strong>Data Science with Python</li><li><strong> Course 02: </strong>Data Science &amp; Machine Learning with Python</li><li><strong> Course 03: </strong>R Programming for Data Science</li><li><strong> Course 04: </strong>Python for Data Analysis</li><li><strong> Course 05: </strong>Machine Learning for Predictive Maps in Python and Leaflet</li><li><strong> Course 06: </strong>Learn to Use Python for Spatial Analysis in ArcGIS</li><li><strong> Course 07: </strong>Python Programming for Everybody</li><li><strong> Course 08: </strong>Encryption</li><li><strong> Course 09: </strong>GDPR Data Protection Level 5</li><li><strong> Course</strong> 10: Decision Making and Critical Thinking</li><li><strong> Course 11: </strong>Time Management</li></ul><p>Enrol now in Data Science to advance your career, and use the premium study materials from Apex Learning.<br /><strong>Certificate:</strong></p><ul><li><strong>PDF Certificate: Free (Previously it was £6*11 = £66)</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course: Previously it was £10)</strong></li></ul><p>The bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your Data Scienceexpertise and essential knowledge, which will assist you in reaching your goal.</p><p><strong>Curriculum:</strong></p><p><strong> Course 01: R Programming for Data Science</strong></p><ul><li>Unit 01: Data Science Overview</li><li>Unit 02: R and RStudio</li><li>Unit 03: Introduction to Basics</li><li>Unit 04: Vectors</li><li>Unit 05: Matrices</li><li>Unit 06: Factors</li><li>Unit 07: Data Frames</li><li>Unit 08: Lists</li><li>Unit 09: Relational Operators</li><li>Unit 10: Logical Operators</li><li>Unit 11: Conditional Statements</li><li>Unit 12: Loops</li><li>Unit 13: Functions</li><li>Unit 14: R Packages</li><li>Unit 15: The Apply Family - lapply</li><li>Unit 16: The apply Family - sapply &amp; vapply</li><li>Unit 17: Useful Functions</li><li>Unit 18: Regular Expressions</li><li>Unit 19: Dates and Times</li><li>Unit 20: Getting and Cleaning Data</li><li>Unit 21: Plotting Data in R</li><li>Unit 22: Data Manipulation with dplyr</li></ul><p>And much more...</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        120 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">
                            <p>Anyone from any background can enrol in this Data Science bundle.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Data Science course has been designed to be fully compatible with tablets and smartphones.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Having this expertise will increase the value of your CV and open you up to multiple job sectors.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>You will get the Hard Copy certificate for the title course (<strong>R Programming for Data Science</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p><p>Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

Apex Learning

Data Science with Python Course

General Admission

<strong>Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.</strong>
        <p>Unlock the power of deep learning and take your machine learning skills to the next level with our comprehensive course on deep neural networks.

This hands-on course will provide you with a solid understanding of the fundamentals of deep learning, including artificial neural networks, activation functions, bias, data, and loss functions. You will learn the basics of Python, with a focus on data science, as well as the essential tools for cleaning and examining data, plotting with Matplotlib, and working with NumPy and Pandas.

With this foundation in place, you will dive deep into the world of deep learning, starting with the MP Neuron model and progressing to the Perceptron, the Sigmoid Neuron, and the Universal Approximation Theorem. You will explore common activation functions, such as ReLU and SoftMax, and learn how to apply them in real-world applications.

Through a series of practical exercises, you will gain hands-on experience with TensorFlow 2.x, one of the most popular deep learning frameworks in use today. You will learn how to create and train deep neural networks, evaluate their performance, and fine-tune them for optimal results.

By the end of the course, you will be well on your way to becoming a deep learning expert in no time.</p>
        <h2>What You Will Learn</h2><p>Learn about the fundamentals of Python and some of its well-known libraries<br>Understand the fundamentals of deep learning and neural networks<br>Build and train your own deep neural network models<br>Learn different activation functions and optimization algorithms<br>Learn techniques for improving model performance and reducing overfitting<br>Apply deep learning to real-world problems in various fields</p>
        <h2>Audience</h2><p>This course is suitable for anyone interested in exploring the field of deep learning and building a solid foundation in artificial neural networks. No prior experience in programming or machine learning is required, making it an ideal starting point for beginners. It is ideal for students, professionals, and anyone who wants to enhance their skills and stay up-to-date with the latest developments in the field of artificial intelligence. Whether you are looking to kickstart your career or simply want to explore the exciting world of deep learning, this course is a great choice.</p>
        <h2>Approach</h2><p>The course adopts a practical, hands-on learning approach that focuses on building intuition and mastery through implementation. Students will work through a series of exercises and projects designed to help them understand the fundamental concepts of deep learning and develop their skills in Python programming, data analysis, and neural network design.</p>
        <h2>Key Features</h2><p>Practical hands-on exercises to build intuition on deep neural networks * Learn essential concepts in deep learning through practical applications * A comprehensive understanding of neural network architecture and design</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Deep-Learning---Crash-Course-2023</p>
        <h2>About the Author</h2><h3>Manifold AI Learning</h3><p>Manifold AI Learning is an online academy with the goal to empower students with the knowledge and skills that can be directly applied to solving real-world problems in data science, machine learning, and artificial intelligence.

With a curated curriculum and a hands-on guide, you will always be an industry-ready professional.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><br><h4>1. Welcome on Board</h4><p></p><br><h4>2. Getting the Basics Right</h4><p></p><br><h4>3. Python Crash Course on Basics</h4><p></p><br><h4>4. Python for Data Science - Crash Course</h4><p></p><br><h4>5. MP Neuron Model</h4><p></p><br><h4>5. MP Neuron Model</h4><p></p><br><h4>6. MP Neuron in Python</h4><p></p><br><h4>7. Summary of MP Neuron</h4><p></p><br><h4>7. Summary of MP Neuron</h4><p></p><br><h4>8. Perceptron</h4><p></p><br><h4>9. Perceptron in Python</h4><p></p><br><h4>10. Sigmoid Neuron</h4><p></p><br><h4>11. Sigmoid Neuron Implement with Python</h4><p></p><br><h4>12. Basic Probability</h4><p></p><br><h4>12. Basic Probability</h4><p></p><br><h4>13. Why Deep Neural Networks - Intuition</h4><p></p><br><h4>13. Deep Neural Networks</h4><p></p><br><h4>14. Universal Approximation Theorem</h4><p></p><br><h4>15. Deep Learning with TensorFlow 2.x</h4><p></p><br><h4>16. Activation Functions in Deep Learning Neural Networks</h4><p></p><br><h4>17. Applying Deep Learning</h4><p></p>

Packt

Keras

Data Science

Python

Pandas

TensorFlow

Deep Learning

Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.

Deep Learning - Crash Course 2023

<p>The world of machine learning and data analysis is rapidly evolving, and the demand for skilled professionals is soaring. If you're looking to level up your skills in this field, then this Deep Learning Neural Network with R course is just what you need. This course is designed to help you master the fundamentals of deep learning neural networks, with a specific focus on R programming.</p><p>Through this course, you'll learn how to build single-layer and multi-layer neural networks, which are critical for many machine learning applications. You'll start by diving into single-layer neural networks for agricultural projects, where you'll learn how to develop models to predict crop yields. Then, you'll move on to multi-layer neural networks for war casualties analysis, where you'll learn how to identify patterns and make predictions.</p><p>With hands-on projects and real-world examples, you'll gain the practical experience and theoretical understanding you need to take on complex deep learning projects. Whether you're an experienced data analyst or just starting in the field, this course is perfect for anyone looking to expand their knowledge and expertise in deep learning neural networks with R.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Understanding of single-layer and multi-layer neural networks</li><li>Knowledge of R programming for neural network applications</li><li>Implementation of neural networks in real-world projects</li><li>Familiarity with agriculture and war datasets for neural network modelling</li><li>Ability to evaluate neural network model accuracy and performance</li></ul><p>The Deep Learning Neural Network with R course is designed to provide learners with a comprehensive understanding of how to build and evaluate neural networks using R programming language. The course includes four modules that cover single-layer and multi-layer neural networks applied to agriculture and war datasets. Each module contains practical hands-on projects that allow learners to gain real-world experience in neural network development and evaluation. By the end of the course, learners will have a solid understanding of neural network concepts, R programming language, and practical experience with real-world datasets.</p><p><strong>â±â±Deep Learning Neural Network with R</strong><strong> Course Curriculum </strong></p><ul><li><strong>Section 01: </strong>Single Layer Neural Networks Project - Agriculture (Part - 1)</li><li><strong>Section 02: </strong>Single Layer Neural Networks Project - Agriculture (Part - 2)</li><li><strong>Section 03: </strong>Multi-Layer Neural Networks Project - Deaths in wars (Part - 1)</li><li><strong>Section 04: </strong>Multi-Layer Neural Networks Project - Deaths in wars (Part - 2)</li></ul><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</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>Data analysts and scientists seeking to expand their knowledge of neural networks and R programming</li><li>Professionals interested in applying neural networks to agriculture or war datasets</li><li>Students and researchers interested in deep learning and machine learning techniques</li><li>Anyone looking to enhance their skills in data analysis and modelling using neural networks and R programming</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal entry requirements for the course, with enrollment open to anyone!</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst</li><li>Machine Learning Engineer</li><li>Data Scientist</li><li>Artificial Intelligence Developer</li><li>Research Scientist</li></ul><p>Entry-level positions such as Data Analysts can expect to earn between £25,000 to £35,000 per annum, whereas senior-level positions such as Machine Learning Engineers can earn upwards of £70,000 per annum.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

One Education

Deep Learning Neural Network with R Course

<strong>This beginner's level course on Pandas acquaints you to the data analysis fundamentals using Pandas library and working with Pandas primary data structures, Series and DataFrame. Learn all about data input using indexing and filtering, data preprocessing, and handle missing values and duplicate rows to transform your data more efficiently with the help of this hands-on course.</strong>
        <p>If you work in any functional areas of data analysis, machine learning, and artificial intelligence, you will want to be familiar with or master Pandas. Pandas is a popular Python library used for data analysis and manipulation, commonly used with data analysis, artificial intelligence, and machine learning. Pandas enables quick and efficient data manipulation, aggregation, pivoting, and flexible time series.

This course will introduce you to the basics of data analysis using the Pandas library. You will learn to work with two primary data structures in Pandas, Series and Data Frame. Then, we will take a look at how to read data from a file and explore input data using indexing and filtering, at which point you will be ready for data preprocessing. Next, we will focus on handling missing values and duplicate rows and transforming data into a more efficient format. You will also discover how to manipulate data and data processing. Finally, we will dive into creating simple plots to visualize the data.

By the end of this course, you can use OOPs paradigm to create class hierarchies with the OOP design process. You can design and implement Python programs for complex issues and make good use of the features like classes and inheritance.

All resources are available at: https://github.com/PacktPublishing/pandas-for-Beginners---A-Quick-Guide</p>
        <h2>What You Will Learn</h2><p>Learn basic data analysis with Pandas' open-source Python library<br>Use the two primary Pandas data structures, Series and DataFrame<br>Process varied data types and manipulate data with string function<br>Organize input with index and filter, preprocess data with Pandas<br>Format and process different kinds of data most efficiently<br>Manipulate, aggregate, and pivot data flexibly and efficiently</p>
        <h2>Audience</h2><p>This course targets beginner Python developers or those who want to learn and use the Pandas library for Python. Data analysts, project management analysts, and those working in artificial intelligence, machine learning, or data science can benefit from this course immensely. For those managers and executives that handle a lot of data and create analytic reporting material, this course could help them as well in their data science projects. This course assumes no previous experience in Pandas, but a fundamental understanding of basic Python syntax is needed since it is built for Python.</p>
        <h2>Approach</h2><p>The course offers interactive onscreen demonstrations of the Pandas library, its installation, and setup, getting familiar with the usage and manipulation of data with examples and explanations. It is loaded with quizzes and project examples that can be attempted as you learn. It offers individual videos and course files to follow along.</p>
        <h2>Key Features</h2><p>Learn to view data imported from an external source * Understand data analysis basics with Pandas and data structures, Series and DataFrame * Explore data manipulation and processing using Pandas and create simple plots to visualize data</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/pandas-for-Beginners---A-Quick-Guide</p>
        <h2>About the Author</h2><h3>Simon Sez IT</h3><p>Simon Sez IT has offered technical courses for individuals, small businesses, and Fortune 500 companies since 2008, with thousands of employees who can benefit from the easy-to-learn and hands-on software training. It offers over 8,000 video tutorials on a range of software programs.

Simon Sez IT ensures stress-free eLearning and enhanced employee productivity-whether you implement new software or a technological upgrade in your work environment. With over 600,000 students from 180 countries, Simon Sez IT is the preferred online learning choice for individuals and businesses worldwide.</p>
        <h2>Course Outline</h2><h4>1. Introduction to Pandas</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Pandas</p><p style='margin: 0'>This video will look at an introductory overview of the Pandas course and what we will be learning about. It also takes us through an outline of the features and capabilities of Pandas and their use.</p></td></tr><tr><td style='padding: 15px;'><p>2. Pandas Installation</p><p style='margin: 0'>This video demonstrates how to install Pandas and discusses everything that is needed for starting to use Pandas. It takes us through installing Python and the Pandas library and shows how to install Jupyter notebook.</p></td></tr></tbody></table><br><h4>2. Pandas Objects</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Pandas Series</p><p style='margin: 0'>In this video, we will look at an introductory outline of the Pandas series and NumPy arrays, which makes the code fast and optimized.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Pandas Data Frame</p><p style='margin: 0'>In this lesson, you will learn about the second primary data structure called Pandas Data Frame, which is a tabular data structure. We will understand the data frame and its operability. We will also learn about the Pandas dictionary.</p></td></tr><tr><td style='padding: 15px;'><p>3. Reading Data from Files</p><p style='margin: 0'>After learning about the Data Frame, we will look into how to read .csv files, a common datasheet. You will also learn about the pd.read_csv() call function. The author provides learning datasets and how to solve issues by correcting the datasets. The video also explains headers and adding values from the datasets to a data frame.</p></td></tr><tr><td style='padding: 15px;'><p>4. Selection and Filtering Pandas</p><p style='margin: 0'>In this video, you will learn about extracting part of the data frame using the selection operation. We will also look at accessing one or more columns using "[] and iloc and loc." We will look at how to select a part of the data frame in detail.</p></td></tr></tbody></table><br><h4>3. Data Preprocessing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Handling Missing Values and Duplicates</p><p style='margin: 0'>During the data analysis, significant time is spent on data cleaning and transformation. Pandas provides the tool to handle such data. In this video, you will learn about handling missing data and duplicates. You will learn to eliminate rows with missing values and fill missing values and look at duplicates similarly.</p></td></tr><tr><td style='padding: 15px;'><p>2. DataFrame Manipulation</p><p style='margin: 0'>Here, you will learn how to rename columns in a data frame. We will look at adding columns to a data frame, concatenating a data frame, and removing columns with the drop method.</p></td></tr><tr><td style='padding: 15px;'><p>3. Data Transformation</p><p style='margin: 0'>In this video, you will learn how to convert String data to a category and discretize a continuous variable and convert it into a categorical feature.</p></td></tr></tbody></table><br><h4>4. Data Processing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Processing Numerical Data for Pandas</p><p style='margin: 0'>In this video, you will learn about the various powerful tools available for numerical and statistical analysis on Pandas. We will look at the described () method, minimum and maximum values, and obtain the mean, median, variance, and standard deviation.</p></td></tr><tr><td style='padding: 15px;'><p>2. Processing String Format</p><p style='margin: 0'>In this lesson, we will continue processing data, but this time with string format data. String features have their operations. We will look at all the powerful tools on Pandas for handling string data.</p></td></tr><tr><td style='padding: 15px;'><p>3. Processing-Date/Time Format</p><p style='margin: 0'>Here, we will understand how to process and manipulate date/time data values. We will learn the most common and practical methods to manipulate and analyze date/time data.</p></td></tr></tbody></table><br><h4>5. Data Processing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Visualize your Data</p><p style='margin: 0'>In this video, you will learn about data visualization. We will look at the basic and simple plots like the scatter plot, line plot, histogram, bar chart, and pie chart.</p></td></tr></tbody></table>

Data Analysis

This beginner's level course on Pandas acquaints you to the data analysis fundamentals using Pandas library and working with Pandas primary data structures, Series and DataFrame. Learn all about data input using indexing and filtering, data preprocessing, and handle missing values and duplicate rows to transform your data more efficiently with the help of this hands-on course.

Pandas for Beginners - A Quick Guide

<strong>This course starts with the basics of Recurrent Neural Networks (RNNs) with Python and then teaches you how to build them by taking you through various exercises and projects. You will be able to test your skills by completing two exciting projects: creating an automatic book writer and a stock price prediction application.</strong>
        <p>With the exponential growth of user-generated data, there is a strong need to move beyond standard neural networks in order to perform tasks such as classification and prediction. Here, architectures such as RNNs, Gated Recurrent Units (GRUs), and Long Short Term Memory (LSTM) are the go-to options. Hence, for any deep learning engineer, mastering RNNs is a top priority.

This course begins with the basics and will gradually equip you with not only the theoretical know-how but also the practical skills required to successfully build, train, and implement RNNs. This course contains several exercises on topics such as gradient descents in RNNs, GRUs, LSTM, and so on. This course also introduces you to implementing RNNs using TensorFlow.

The course culminates in two exciting and realistic projects: creating an automatic book writer and a stock price prediction application. By the end of this course, you will be equipped with all the skills required to confidently use and implement RNNs in your applications.

The code bundle for this course is available at https://github.com/AISCIENCES/mastering_recurrent_neural_networks</p>
        <h2>What You Will Learn</h2><p>Gain an overview of deep neural networks<br>Understand the fundamentals of RNN architectures<br>Train real-world datasets using different RNN architectures<br>Implement RNNs, LSTM, and GRUs through hands-on exercises<br>Create and compile RNN models in TensorFlow<br>Perform text classification using RNNs and TensorFlow</p>
        <h2>Audience</h2><p>As this course begins with the basics, no prior knowledge in RNNs is required. However, prior experience in Python would be beneficial. Whether you are a beginner, a seasoned data scientist looking to get started with RNNs, business analysts, or if you simply want to implement RNNs in your projects, this course is for you.</p>
        <h2>Approach</h2><p>Through carefully designed modules, a simple-to-understand theory, engaging hands-on exercises, and realistic implementations of RNNs in projects, this course will help you master RNNs.</p>
        <h2>Key Features</h2><p>Understand and apply fundamentals of recurrent neural networks * Implement RNNs and related architectures on real-world datasets * Train RNNs for real-world applications-automatic book writer and stock price prediction</p>
        <h2>Github Repo</h2><p>https://github.com/AISCIENCES/mastering_recurrent_neural_networks</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Applications of RNN</h4><p></p><br><h4>3. Deep Neural Network (DNN) Overview</h4><p></p><br><h4>4. RNN Architecture</h4><p></p><br><h4>5. Gradient Descent in RNN</h4><p></p><br><h4>6. RNN Implementation</h4><p></p><br><h4>7. Sentiment Classification Using RNN</h4><p></p><br><h4>8. Vanishing Gradients in RNN</h4><p></p><br><h4>9. TensorFlow</h4><p></p><br><h4>10. Project 1: Book Writer</h4><p></p><br><h4>11. Project 2: Stock Price Prediction</h4><p></p><br><h4>12. Further Reading and Resources</h4><p></p>

This course starts with the basics of Recurrent Neural Networks (RNNs) with Python and then teaches you how to build them by taking you through various exercises and projects. You will be able to test your skills by completing two exciting projects: creating an automatic book writer and a stock price prediction application.

Deep Learning: Recurrent Neural Networks with Python

<p>Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn.<br />Python's simple syntax suits desktop, web, and business applications. Python's design philosophy emphasizes readability and usability.</p> <p>The average python developer salary in the United Kingdom is £55,000 per year or £28.21 per hour. Entry-level positions start at £42,500 per year, while most experienced workers make up to £77,500 per year.</p> <p>So enrol in this Python Coding Specialist (PCS) bundle and become a specialist.</p> <p>Along with this Python course, you will get <strong>10 premium courses, an original hardcopy</strong>, <strong>11 PDF Certificates (Main Course + Additional Courses) </strong>Student ID card as gifts.</p> <p><strong>This Python Coding Specialist (PCS) Bundle Consists of the following Premium courses: </strong></p> <ul> <li><strong></strong><strong> Course 01: </strong>Python Programming for Everybody</li><li><strong></strong><strong> Course 02: </strong>Data Science with Python</li><li><strong></strong><strong> Course 03: </strong>Machine Learning with Python</li><li><strong></strong><strong> Course 04: </strong>Learn to Use Python for Spatial Analysis in ArcGIS</li><li><strong></strong><strong> Course 05: </strong>Higher Order Functions in Python - Level 03</li><li><strong></strong><strong> Course 06: </strong>Introduction to Data Analysis</li><li><strong></strong><strong> Course 07: </strong>Data Structures Complete Course</li><li><strong></strong><strong> Course 08: </strong>Basic Google Data Studio</li><li><strong></strong><strong> Course 09: </strong>Business Intelligence and Data Mining Masterclass</li><li><strong></strong><strong> Course 10: </strong>Cyber Security Incident Handling and Incident Response</li><li><strong></strong><strong> Course</strong> 11: Decision Making and Critical Thinking </li></ul> <p>Enrol now in Python Coding Specialist (PCS) to advance your career, and use the premium study materials from Apex Learning.</p><p><strong> Python Coding Specialist (PCS) </strong></p> <p>The bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your Python Coding Specialist (PCS) expertise and essential knowledge, which will assist you in reaching your goal. Moreover, you can learn from any place in your own time without travelling for classes.</p> <p><strong>Course Curriculum : </strong></p> <p><strong>Module 01 <br /></strong></p> <ul> <li>A Installing Python</li><li>Documentation</li><li>Command Line</li><li>Variables</li><li>Simple Python Syntax</li><li>Keywords</li><li>Import Module</li></ul> <p><strong>Module 02</strong></p> <ul> <li>Additional Topics</li><li>If Elif Else</li><li>Iterable</li><li>For</li><li>Loops</li><li>Execute</li><li>Exceptions<strong><br /></strong></li></ul> <p><strong>Module 03 <br /></strong></p> <p><strong>Module 04 <br /></strong></p> <p><strong>Module 05 </strong></p> <p><strong>Certificate:</strong></p> <ul> <li><strong>PDF Certificate: Free (Previously it was £6*11 = £66)</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course: Previously it was £10)</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        110 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">
                             <p><strong> Python Coding Specialist (PCS) </strong></p> <p>Anyone from any background can enrol in this Python Coding Specialist (PCS) bundle.</p> 
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                             <p><strong> Python Coding Specialist (PCS) </strong></p> <p>This Python Coding Specialist (PCS) course has been designed to be fully compatible with tablets and smartphones.</p> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <p><strong> Python Coding Specialist (PCS) </strong></p> <p>Having this expertise will increase the value of your CV and open you up to multiple job sectors.</p> 
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>You will get the Hard Copy certificate for the title course (<strong>Python Programming for Everybody</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p> <p><strong>Please Note:</strong> The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

Python Coding Specialist (PCS)

<p><strong>Earn up to 100k as a 'coder' or Your 100% Money Back</strong></p><p>Did you know the UK tech sector is booming, with a projected <strong>1.2 million </strong>job openings by 2025? The demand for skilled coders is soaring, with an average salary in the UK sitting comfortably above <strong>£51,000 annually</strong>, with some roles reaching over <strong>£100,000!</strong> Are you ready to join this exciting field and unlock your earning potential? This Coding Job Guarantee Programme equips you with the essential skills and knowledge sought after by employers.</p><p>Our Coding Job Guarantee Programme is your <strong>all-in-one solution</strong> to learn coding from scratch and also <strong>secure a job</strong> in this exciting industry. With our unique blend of comprehensive courses and career support, your dream job is within reach. We understand the challenges of transitioning into a new field, which is why our program is tailored to guide you every step of the way - from mastering core skills to landing your first job as a coder. By choosing our Coding Job Guarantee Programme, you're not just gaining skills; <strong>you're gaining a </strong><strong>lifelong career partner</strong>.</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 Programmers, 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 Coding Job Guarantee Programme today and start your journey to becoming a successful Coding Programmer!</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 Certified Coding Job Guarantee Programme:</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 Coding experience, we will guide you towards honing your skills and developing a strong foundation in Coding.<br /><br /></li><li><strong>One-On-One Consultation:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Coding 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 Certified Coding Job Guarantee Programme. 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.</li></ul><p><strong>Courses we will provide once you enrol in the program:</strong></p><ul><li><strong>Course 01: </strong>Coding with HTML, CSS, &amp; Javascript</li><li><strong>Course 02: </strong>Python Programming for Everybody</li><li><strong>Course 03: </strong>Ultimate PHP &amp; MySQL Web Development &amp; OOP Coding</li><li><strong>Course 04: </strong>jQuery: JavaScript and AJAX Coding Bible</li><li><strong>Course 05: </strong>C++ Development: The Complete Coding Guide</li><li><strong>Course 06: </strong>HTML Web Development for Beginners</li><li><strong>Course 07: </strong>SQL Programming Masterclass</li><li><strong>Course 08: </strong>JavaScript Foundations for Everyone</li><li><strong>Course 09: </strong>Basic Node.JS</li><li><strong>Course 10: </strong>Master JavaScript with Data Visualization</li><li><strong>Course 11: </strong>Ionic &amp; Angular JS: Mobile and Web Development</li><li><strong>Course 12: </strong>Building a TodoMVC Application in Vue, React and Angular</li><li><strong>Course 13: </strong>Encryption</li><li><strong>Course 14: </strong>Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li><strong>Course 15: </strong>CompTIA A+ (220-1002)</li><li><strong>Course 16: </strong>Web Application Penetration Testing</li><li><strong>Course 17: </strong>Secure Programming of Web Applications - Developers and TPMs</li><li><strong>Course 18: </strong>Microsoft SQL Server Development for Everyone</li><li><strong>Course 19: </strong>The Complete MySQL Server from Scratch: Bootcamp</li><li><strong>Course 20: </strong>Bash Scripting, Linux and Shell Programming</li><li><strong>Course 21: </strong>JavaScript Project - Game Development with JS</li><li><strong>Course 22: </strong>R Programming for Data Science</li><li><strong>Course 23: </strong>Cyber Security Awareness 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 Coding Job Guarantee Programme is </strong><strong>completed in 9 easy steps:</strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the Coding Job Guarantee 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<strong> 23 Premium Courses</strong> related to Coding. These courses will teach you the knowledge and skills required to become a successful Coding 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!</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.</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.</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 programming/development industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently.</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.</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.</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>.</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.</p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in Coding, 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 Coding 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">
                            <p>This Coding Job Guarantee Programme Course is suitable for:</p><ul><li>Beginners with no prior programming experience who are looking to enter the world of coding.</li><li>Intermediate programmers who wish to expand their skills and explore Coding's extensive usage.</li><li>IT professionals seeking to diversify their skill sets and stay relevant in today's tech-driven market.</li><li>Data analysts and scientists who want to leverage PCoding's powerful libraries for their data processing needs.</li><li>Students and recent graduates looking to enhance their employability in the thriving tech industry.</li><li>Entrepreneurs who wish to understand and utilise Coding to automate tasks and analyse data for their businesses.</li><li>Anyone with a keen interest in Coding and a desire to delve into its applications in diverse fields like web development, data science, machine learning, and more.</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">
                            <p>This Coding Job Guarantee Programme will help you get a job in different roles, such as:</p><ul><li>Full-Stack Developer: £35,000 - £100,000 annually</li><li>Front-End Developer: £25,000 - £70,000 annually</li><li>PHP Developer: £30,000 - £80,000 annually</li><li>JavaScript Developer: £30,000 - £90,000 annually</li><li>C++ Software Engineer: £40,000 - £100,000 annually</li><li>Cyber Security Analyst: £40,000 - £100,000 annually</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>

Academy for Health and Fitness

Clinical Coding

Coding For Kids

Computer Programming

Medical Coding

Coding Interview

Trainers Course

Coding Job Guarantee Programme

<p><strong>Earn up to 60k as a 'IT Security Analyst' or Your 100% Money Back</strong></p><p>The field of IT security is constantly developing, with the UK experiencing a considerable increase in cyber threats, emphasising the necessity for effective IT security measures. With over <strong>800,000</strong> IT security job openings expected by 2024 and an average salary of around <strong>£55,000 </strong>for IT Security professionals, the field is not just lucrative but also in high demand. This spike in demand highlights the critical need for qualified professionals capable of protecting digital infrastructures. Are you ready to tackle the complexity of IT security and make a difference?</p><p>This IT Security Job Guarantee Programme is your <strong>all-in-one</strong> solution to learn IT Security from scratch and also <strong>secure a job</strong> in this exciting industry. With our unique blend of comprehensive courses and career support, your dream job is within reach. We understand the challenges of transitioning into a new field, which is why our program is tailored to guide you every step of the way - from mastering core skills to landing your first job as an IT security specialist. By choosing our IT Security Job Guarantee Programme, you're not just gaining skills; <strong>you're gaining a lifelong career partner.</strong></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 Programmers, 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 IT Security Job Guarantee Programme today and start your journey to become successful in the IT Security field!</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 Certified IT Security Job Guarantee Programme:</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 IT Security experience, we will guide you towards honing your skills and developing a strong foundation in the IT Security field.<br /><br /></li><li><strong>One-On-One Consultation:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the IT Security 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 Certified IT Security Professional field. 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.</li></ul><p><strong>Courses we will provide once you enrol in the program:</strong></p><ul><li><strong>Course 01: </strong>IT Support Technician</li><li><strong>Course 02: </strong>Functional Skills IT</li><li><strong>Course 03: </strong>IT Administration and Networking</li><li><strong>Course 04: </strong>Cyber Security Programming</li><li><strong>Course 05: </strong>Cyber Security Advanced Training</li><li><strong>Course 06: </strong>Cyber Security Incident Handling and Incident Response</li><li><strong>Course 07: </strong>General Data Protection Regulation (GDPR) &amp; Cyber Security Management</li><li><strong>Course 08: </strong>Data Center Training Essentials: General Introduction</li><li><strong>Course 09: </strong>Understanding Data Protection and Data Security</li><li><strong>Course 10: </strong>CompTIA Network</li><li><strong>Course 11: </strong>CompTIA IT Fundamentals ITF+ (FCO-U61)</li><li><strong>Course 12: </strong>CompTIA A+ (220-1001)</li><li><strong>Course 13: </strong>CompTIA A+ (220-1002)</li><li><strong>Course 14: </strong>CompTIA Network+ Certification (N10-007)</li><li><strong>Course 15: </strong>CompTIA Security+ (SY0-601)</li><li><strong>Course 16: </strong>Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li><strong>Course 17: </strong>CompTIA CySA+ Cybersecurity Analyst (CS0-002)</li><li><strong>Course 18: </strong>CompTIA PenTest+ (Ethical Hacking)</li><li><strong>Course 19: </strong>Secure Programming of Web Applications</li><li><strong>Course 20: </strong>Cisco ASA Firewall Training</li><li><strong>Course 21: </strong>IGCSE ICT: Network Essentials</li><li><strong>Course 22: </strong>Information Management</li><li><strong>Course 23: </strong>Cyber Security Law</li><li><strong>Course 24: </strong>Cyber Security Incident Handling and Incident Response</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 is IT Security Job Guarantee Programme</strong><strong> completed in 9 easy steps:</strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the IT Security Job Guarantee Programme. 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<strong> 24 Premium Courses</strong> related to IT Security field. These courses will teach you the knowledge and skills required to become a successful IT Security Professional. 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.</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.</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 programming/development industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently.</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.</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.</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>.</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.</p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in the IT Security field, 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 IT Security field 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">
                            <p>This IT Security Job Guarantee Programme Course is suitable for:</p><ul><li>Beginners with no prior programming experience who are looking to enter the world of coding.</li><li>Intermediate programmers who wish to expand their skills and explore IT Security's extensive usage.</li><li>IT professionals seeking to diversify their skill sets and stay relevant in today's tech-driven market.</li><li>Data analysts and scientists who want to leverage IT Security's powerful libraries for their data processing needs.</li><li>Students and recent graduates looking to enhance their employability in the thriving tech industry.</li><li>Entrepreneurs who wish to understand and utilise IT Security to automate tasks and analyse data for their businesses.</li><li>Anyone with a keen interest in IT Security programming and a desire to delve into its applications in diverse fields like web development, data science, machine learning, and more.</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>IT Support Specialist: £25,000 - £55,000 annually</li><li>Network Administrator: £30,000 - £70,000 annually</li><li>Cyber Security Consultant: £40,000 - £90,000 annually</li><li>GDPR Compliance Officer: £35,000 - £75,000 annually</li><li>Cloud Security Engineer: £45,000 - £100,000 annually</li><li>Penetration Tester: £35,000 - £95,000 annually</li><li>Cyber Security Incident Responder: £40,000 - £85,000 annually</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>

Certified in Cybersecurity (CC)

IT Certifications

Certificate in Information Security Management Principles (CISMP)

Cyber Security

Cyber Security Foundation

CompTIA Security+

IT Security Job Guarantee Programme

<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

Azure Machine Learning

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

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
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					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Cloud

Machine Learning (ML)

Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Machine Learning - Use in Flutter

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

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