<strong>Learn Python programming by developing robust GUIs and games</strong>
        <p>Coding for beginners and kids is now made exciting and easy. Learn to code fun?games, graphics and graphical user interfaces (GUIs) in Python, one of the most powerful programming languages known for its speed, flexibility, and ease-of-use. The Python coding language integrates well with other platforms and runs on almost all modern devices. This OSI-approved open-source language allows free use and distribution (including commercial distribution).
This course helps you delve into the graphics and UX (user experience) aspects of Python. You'll get up to speed with this computer language in a hands-on manner by drawing shapes, coding a simple game, and designing GUIs (graphical user interfaces), including a functional GUI for a temperature converter app. The course will also guide you through analyzing scientific and statistical data. As you advance, you'll get to grips with developing educational software. In addition to this, you'll gain comprehensive insights into accessing and organizing databases, along with understanding how to manage networks.
By the end of this course, you will be well-versed with using not only Python 2 but Python 3 as well for designing immersive games and developing interactive applications and websites.
All the codes and supporting files for this course are available at - https://github.com/PacktPublishing/Project-Based-Python-Programming-For-Kids-and-Beginners</p>
        <h2>What You Will Learn</h2><p>Build simple Python-based games using programming loops and functions<br>Draw graphics using the Python module Turtle<br>Develop Python graphical user interfaces with Tkinter<br>Understand how to work with Python 2 and 3<br>Use the in-built Python modules for your projects<br>Use Microsoft Visual Studio Code-a powerful IDLE for programming and development in Python and other languages</p>
        <h2>Audience</h2><p>If you are interested in developing your Python skills, working through real-life programming projects, or building exciting games and GUIs, then this course is for you.</p>
        <h2>Approach</h2><p>The course provides detailed instructions for everything from Python installation to getting started with Microsoft Visual Code. It features helpful coding instructions, along with code examples. Each video will teach you a new programming?concept?that you can apply in real time. The course also helps you reinforce your learning with fun quizzes.</p>
        <h2>Key Features</h2><p>Learn the basics of Python game programming * Develop intuitive and efficient Python GUIs * Create simple yet practical applications in Python</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/project-based-python-programming-for-kids-and-beginners</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. Introduction to Project Based Python Programming</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to the Course & Me</p><p style='margin: 0'>Introduction to Project Based Python Programming: Introduction to the Course & Me</p></td></tr><tr><td style='padding: 15px;'><p>2. Python Installation for Windows</p><p style='margin: 0'>Introduction to Project Based Python Programming: Python Installation for Windows</p></td></tr><tr><td style='padding: 15px;'><p>3. Python Installation for Other OS</p><p style='margin: 0'>Introduction to Project Based Python Programming: Python Installation for Other OS</p></td></tr><tr><td style='padding: 15px;'><p>4. Online Python IDLE</p><p style='margin: 0'>Introduction to Project Based Python Programming: Online Python IDLE</p></td></tr><tr><td style='padding: 15px;'><p>5. Hello World in Python</p><p style='margin: 0'>Introduction to Project Based Python Programming: Hello World in Python</p></td></tr><tr><td style='padding: 15px;'><p>6. Python Coding with Visual Studio</p><p style='margin: 0'>Introduction to Project Based Python Programming: Python Coding with Visual Studio</p></td></tr><tr><td style='padding: 15px;'><p>7. Some Common Python Terms</p><p style='margin: 0'>Introduction to Project Based Python Programming: Some Common Python Terms</p></td></tr><tr><td style='padding: 15px;'><p>8. Conclusion to Section 1</p><p style='margin: 0'>Introduction to Project Based Python Programming: Conclusion to Section 1</p></td></tr></tbody></table><br><h4>2. Diving into the Python Basics</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Basic Arithmetic in Python</p><p style='margin: 0'>Diving into the Python Basics: Basic Arithmetic in Python</p></td></tr><tr><td style='padding: 15px;'><p>2. Welcome to the World of Data Types</p><p style='margin: 0'>Diving into the Python Basics: Welcome to the World of Data Types</p></td></tr><tr><td style='padding: 15px;'><p>3. Random Number Generation</p><p style='margin: 0'>Diving into the Python Basics: Random Number Generation</p></td></tr><tr><td style='padding: 15px;'><p>4. Working With Strings</p><p style='margin: 0'>Diving into the Python Basics: Working With Strings</p></td></tr><tr><td style='padding: 15px;'><p>5. Get User Inputs</p><p style='margin: 0'>Diving into the Python Basics: Get User Inputs</p></td></tr><tr><td style='padding: 15px;'><p>6. Tuples</p><p style='margin: 0'>Diving into the Python Basics: Tuples</p></td></tr><tr><td style='padding: 15px;'><p>7. Lists</p><p style='margin: 0'>Diving into the Python Basics: Lists</p></td></tr><tr><td style='padding: 15px;'><p>8. Dictionaries</p><p style='margin: 0'>Diving into the Python Basics: Dictionaries</p></td></tr><tr><td style='padding: 15px;'><p>9. Defining our Own Functions in Python-Basic</p><p style='margin: 0'>Diving into the Python Basics: Defining our Own Functions in Python-Basic</p></td></tr><tr><td style='padding: 15px;'><p>10. Some More On Functions</p><p style='margin: 0'>Diving into the Python Basics: Some More On Functions</p></td></tr><tr><td style='padding: 15px;'><p>11. Conclusions to Section 2</p><p style='margin: 0'>Diving into the Python Basics: Conclusions to Section 2</p></td></tr></tbody></table><br><h4>3. Decision Making with Programming</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Rationale for this section</p><p style='margin: 0'>Decision Making with Programming: Rationale for this section</p></td></tr><tr><td style='padding: 15px;'><p>2. if-else loops</p><p style='margin: 0'>Decision Making with Programming: if-else loops</p></td></tr><tr><td style='padding: 15px;'><p>3. elif in if-else loops</p><p style='margin: 0'>Decision Making with Programming: elif in if-else loops</p></td></tr><tr><td style='padding: 15px;'><p>4. while loop</p><p style='margin: 0'>Decision Making with Programming: while loop</p></td></tr><tr><td style='padding: 15px;'><p>5. for loop</p><p style='margin: 0'>Decision Making with Programming: for loop</p></td></tr><tr><td style='padding: 15px;'><p>6. More on Loops</p><p style='margin: 0'>Decision Making with Programming: More on Loops</p></td></tr><tr><td style='padding: 15px;'><p>7. Nested For Loop</p><p style='margin: 0'>Decision Making with Programming: Nested For Loop</p></td></tr><tr><td style='padding: 15px;'><p>8. Build a Number Guessing Game!!-Basic</p><p style='margin: 0'>Decision Making with Programming: Build a Number Guessing Game!!-Basic</p></td></tr><tr><td style='padding: 15px;'><p>9. Build a Number Guessing Game!!-Advanced</p><p style='margin: 0'>Decision Making with Programming: Build a Number Guessing Game!!-Advanced</p></td></tr><tr><td style='padding: 15px;'><p>10. OPTIONAL:Build a Number Guessing Game!!-(More) Advanced</p><p style='margin: 0'>Decision Making with Programming: OPTIONAL:Build a Number Guessing Game!!-(More) Advanced</p></td></tr><tr><td style='padding: 15px;'><p>11. Conclusion to Section 3</p><p style='margin: 0'>Decision Making with Programming: Conclusion to Section 3</p></td></tr></tbody></table><br><h4>4. Graphics with Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Working With Graphics in Python</p><p style='margin: 0'>Graphics with Python: Working With Graphics in Python</p></td></tr><tr><td style='padding: 15px;'><p>2. Draw a Square</p><p style='margin: 0'>Graphics with Python: Draw a Square</p></td></tr><tr><td style='padding: 15px;'><p>3. The Idea Behind Angles</p><p style='margin: 0'>Graphics with Python: The Idea Behind Angles</p></td></tr><tr><td style='padding: 15px;'><p>4. Draw a Rectangle Using Loops</p><p style='margin: 0'>Graphics with Python: Draw a Rectangle Using Loops</p></td></tr><tr><td style='padding: 15px;'><p>5. Create a Rectangle Function</p><p style='margin: 0'>Graphics with Python: Create a Rectangle Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Code Along1: Draw an Equilateral Triangle</p><p style='margin: 0'>Graphics with Python: Code Along1: Draw an Equilateral Triangle</p></td></tr><tr><td style='padding: 15px;'><p>7. Code Along2: Draw an Equilateral Triangle With Loops</p><p style='margin: 0'>Graphics with Python: Code Along2: Draw an Equilateral Triangle With Loops</p></td></tr><tr><td style='padding: 15px;'><p>8. Code an Irregular Shape: Start a Snowflake</p><p style='margin: 0'>Graphics with Python: Code an Irregular Shape: Start a Snowflake</p></td></tr><tr><td style='padding: 15px;'><p>9. Snowflakes!!</p><p style='margin: 0'>Unsupervised Learning in Python: Hierarchical Clustering-practical</p></td></tr><tr><td style='padding: 15px;'><p>10. Conclusions to Section 4</p><p style='margin: 0'>Graphics with Python: Conclusions to Section 4</p></td></tr></tbody></table><br><h4>5. GUI Programming with Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Start with GUIs in Python</p><p style='margin: 0'>GUI Programming with Python: Start with GUIs in Python</p></td></tr><tr><td style='padding: 15px;'><p>2. Create a Simple Button With tkinter</p><p style='margin: 0'>GUI Programming with Python: Create a Simple Button With tkinter</p></td></tr><tr><td style='padding: 15px;'><p>3. Add Functionality to the Button</p><p style='margin: 0'>GUI Programming with Python: Add Functionality to the Button</p></td></tr><tr><td style='padding: 15px;'><p>4. The Tkinter Grid</p><p style='margin: 0'>GUI Programming with Python: The Tkinter Grid</p></td></tr><tr><td style='padding: 15px;'><p>5. Common Tkinter GUI Widgets</p><p style='margin: 0'>GUI Programming with Python: Common Tkinter GUI Widgets</p></td></tr><tr><td style='padding: 15px;'><p>6. Canvas for Drawing-part1</p><p style='margin: 0'>GUI Programming with Python: Canvas for Drawing-part1</p></td></tr><tr><td style='padding: 15px;'><p>7. Canvas for Drawing-part2</p><p style='margin: 0'>GUI Programming with Python: Canvas for Drawing-part2</p></td></tr><tr><td style='padding: 15px;'><p>8. Simple Expression Evaluator</p><p style='margin: 0'>GUI Programming with Python: Simple Expression Evaluator</p></td></tr><tr><td style='padding: 15px;'><p>9. Start With the Temperature Conversion App</p><p style='margin: 0'>GUI Programming with Python: Start With the Temperature Conversion App</p></td></tr><tr><td style='padding: 15px;'><p>10. Complete the App-1</p><p style='margin: 0'>GUI Programming with Python: Complete the App-1</p></td></tr><tr><td style='padding: 15px;'><p>11. Complete the App-2</p><p style='margin: 0'>GUI Programming with Python: Complete the App-2</p></td></tr></tbody></table>

Packt

Python

Programming

Learn Python programming by developing robust GUIs and games

Project-Based Python Programming For Kids and Beginners

General Admission

Network

Networking

Cisco

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

Python Network Programming for Network Engineers (Python 3)

<strong>This comprehensive course will help you learn how to use the power of Python to evaluate your deep learning-based recommender system data sets based on user ratings and choices with a practical approach to building a deep learning-based recommender system by adopting a retrieval-based approach based on a two-tower model.</strong>
        <p>Recommender systems are used in various areas with commonly recognized examples, including playlist generators for video and music services, product recommenders for online stores and social media platforms, and open web content recommenders. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services.

The course begins with an introduction to deep learning concepts to develop recommender systems and a course overview. The course advances to topics covered, including deep learning for recommender systems, understanding the pros and cons of deep learning, recommendation inference, and deep learning-based recommendation approach. You will then explore neural collaborative filtering and learn how to build a project based on the Amazon Product Recommendation System. You will learn to install the required packages, analyze data for products recommendation, prepare data, and model development using a two-tower approach.

You will learn to implement a TensorFlow recommender and test a recommender model. You will make predictions using the built recommender system.

Upon completion, you can relate the concepts and theories for recommender systems in various domains and implement deep learning models for building real-world recommendation systems.

All resources are available at: https://github.com/PacktPublishing/Recommender-Systems-An-Applied-Approach-using-Deep-Learning</p>
        <h2>What You Will Learn</h2><p>Learn about deep learning and recommender systems<br>Explore the mechanisms of deep learning-based approaches<br>Learn to implement a two-tower model for recommenders<br>Implement TensorFlow to develop a recommender system<br>Learn basic neural network models for recommendations<br>Explore neural collaborative filtering and variational autoencoders</p>
        <h2>Audience</h2><p>This course is designed for individuals looking to advance their skills in applied deep learning, understand relationships of data analysis with deep learning, build customized recommender systems for their applications, and implement deep learning algorithms for recommender systems. Individuals passionate about recommender systems with the help of TensorFlow Recommenders will benefit from this course. Deep learning practitioners, research scholars, and data scientists will also benefit from the course. The prerequisites include a basic to intermediate knowledge of Python and Pandas library.</p>
        <h2>Approach</h2><p>The course is well-structured and explanatory. Every module has engaging content covering necessary theoretical concepts with practical explanations. The lectures are divided into many videos and comprehensively detailed code notebooks, providing a unique hands-on experience using a real-time project. This course is easily understandable, expressive, and self-explanatory, with live coding.</p>
        <h2>Key Features</h2><p>Understand, implement, and evaluate deep learning models for building real-world recommendation systems * Validate, test, and make predictions using recommender systems with the help of TensorFlow Recommenders * Explore the benefits and challenges of deep learning in recommender systems</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Recommender-Systems-An-Applied-Approach-using-Deep-Learning</p>
        
        <h2>Course Outline</h2><h4>1. Introduction</h4><p>This section focuses on introducing the author and presenting a generalized overview of the course content.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. About the Instructor</p><p style='margin: 0'>In this video, we will get a brief outline of the author, AI Sciences, and how they deliver their courses.</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Outline</p><p style='margin: 0'>This brief introductory video will cover the course contents and the concepts you will learn in this course.</p></td></tr></tbody></table><br><h4>2. Deep Learning Foundation for Recommender Systems</h4><p>This section focuses on deep learning methodologies used in recommender systems. Here, we will cover inferences, embeddings, and neutral collaborative filtering aside from VAE and deep learning models.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Introduction</p><p style='margin: 0'>In this video, we will look at a general introduction to deep learning and what is covered in this section under deep learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Overview</p><p style='margin: 0'>This video briefly overviews the contents and topics covered in this module, including deep learning in recommender systems, the pros and cons of deep learning, recommender interference, and more.</p></td></tr><tr><td style='padding: 15px;'><p>3. Deep Learning in Recommender Systems</p><p style='margin: 0'>In this video, we will explore deep learning concepts in recommender systems, migrating from machine learning to deep learning, and capturing non-linear to non-trivial relationships.</p></td></tr><tr><td style='padding: 15px;'><p>4. Inference after Training</p><p style='margin: 0'>In this lecture, you will learn how to deploy the model as a service to infer the likelihood of new interactions.</p></td></tr><tr><td style='padding: 15px;'><p>5. Inference Mechanism</p><p style='margin: 0'>In this lesson, we will understand the generic recommendation systems interference mechanism.</p></td></tr><tr><td style='padding: 15px;'><p>6. Embeddings and User Context</p><p style='margin: 0'>This video focuses on collaborative filtering with the help of deep learning and neural collaborative filtering.</p></td></tr><tr><td style='padding: 15px;'><p>7. Neural Collaborative Filtering</p><p style='margin: 0'>In this video, we will understand neural collaborative filtering and how to incorporate it into our recommender system.</p></td></tr><tr><td style='padding: 15px;'><p>8. VAE Collaborative Filtering</p><p style='margin: 0'>This lesson explores the variational autoencoder for collaborative filtering, using representations in hidden layers.</p></td></tr><tr><td style='padding: 15px;'><p>9. Strengths and Weaknesses of DL Models</p><p style='margin: 0'>In this lesson, you will learn about the strengths and weaknesses of deep learning recommender system models.</p></td></tr><tr><td style='padding: 15px;'><p>10. Deep Learning Quiz</p><p style='margin: 0'>This quiz is based on the deep learning concepts learned so far in this module.</p></td></tr><tr><td style='padding: 15px;'><p>11. Deep Learning Quiz Solution</p><p style='margin: 0'>This is the solution to the quiz based on the deep learning concepts learned so far in this module.</p></td></tr></tbody></table><br><h4>3. Project Amazon Product Recommendation System</h4><p>This module focuses on TensorFlow recommenders, creating a two-tower model, data visualization with WordCloud, creating tensors with DataFrame, query towers, random train test split, and computer loss.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Overview</p><p style='margin: 0'>This is a brief introductory overview of what is being covered in this section, including creating projects, TensorFlow recommenders, libraries, WordCloud, and DataFrame.</p></td></tr><tr><td style='padding: 15px;'><p>2. TensorFlow Recommenders</p><p style='margin: 0'>In this video, you will learn about TensorFlow recommenders used to develop recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>3. Two-Tower Model</p><p style='margin: 0'>In this lesson, we will discuss the two-tower model, which uses user embedding and item embedding in the recommender system.</p></td></tr><tr><td style='padding: 15px;'><p>4. Project Overview</p><p style='margin: 0'>This video outlines the project and will demonstrate how to install the packages required for the project, data preparation, using TensorFlow, evaluation, and recommendation.</p></td></tr><tr><td style='padding: 15px;'><p>5. Download Libraries</p><p style='margin: 0'>In this video, you will learn to implement the Amazon recommender system using the TensorFlow recommender based on specific user behavior. You will learn to download and import particular libraries for the project.</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Visualization with WordCloud</p><p style='margin: 0'>In this video, you will learn how to load a dataset for the project being developed, using WordCloud, importing WordCloud, STOPWORDS, and ImageColorGenerator.</p></td></tr><tr><td style='padding: 15px;'><p>7. Make Tensors from DataFrame</p><p style='margin: 0'>In this video, we will continue to check our dataset using a single username. We will use a part of the DataFrame to do so.</p></td></tr><tr><td style='padding: 15px;'><p>8. Rating Our Data</p><p style='margin: 0'>In this video, you will learn about the next part of the project, which is rating our data.</p></td></tr><tr><td style='padding: 15px;'><p>9. Random Train-Test Split</p><p style='margin: 0'>In this lesson, you will learn to perform the test train split, which will do the training split and then create a prediction; we will use a random dataset (80-20 ratio).</p></td></tr><tr><td style='padding: 15px;'><p>10. Making the Model and Query Tower</p><p style='margin: 0'>In this lecture, you will learn to develop our model and create a query tower to perform retrieval tasks.</p></td></tr><tr><td style='padding: 15px;'><p>11. Candidate Tower and Retrieval System</p><p style='margin: 0'>In this video, you will learn how to create a candidate tower and develop a retrieval system.</p></td></tr><tr><td style='padding: 15px;'><p>12. Compute Loss</p><p style='margin: 0'>Here, we will look at the next step of training our model: the compute loss function.</p></td></tr><tr><td style='padding: 15px;'><p>13. Train and Validation</p><p style='margin: 0'>After entirely developing our model, you will learn to fit and evaluate the model for functionality.</p></td></tr><tr><td style='padding: 15px;'><p>14. Accuracy Versus Recommendations</p><p style='margin: 0'>In this video, we will perform data visualization with the project we created and check the model's accuracy.</p></td></tr><tr><td style='padding: 15px;'><p>15. Making Recommendations</p><p style='margin: 0'>In this lesson, you will learn to create a recommendation and use a brute-force algorithm to generate the recommendation.</p></td></tr></tbody></table>

Data Analysis

TensorFlow

This comprehensive course will help you learn how to use the power of Python to evaluate your deep learning-based recommender system data sets based on user ratings and choices with a practical approach to building a deep learning-based recommender system by adopting a retrieval-based approach based on a two-tower model.

Recommender Systems: An Applied Approach using Deep Learning

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web.</p>Overview<p>This skills-focused combines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Working in a hands-on lab environment led by our expert instructor, attendees will<br />Understand the different kinds of recommender systems<br />Master data-wrangling techniques using the pandas library<br />Building an IMDB Top 250 Clone<br />Build a content-based engine to recommend movies based on real movie metadata<br />Employ data-mining techniques used in building recommenders<br />Build industry-standard collaborative filters using powerful algorithms<br />Building Hybrid Recommenders that incorporate content based and collaborative filtering</p><p>Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.</p></div><div id="course-pills-2"><h4>Getting Started with Recommender Systems</h4><ul><li>Technical requirements</li><li>What is a recommender system?</li><li>Types of recommender systems</li></ul><h4>Manipulating Data with the Pandas Library</h4><ul><li>Technical requirements</li><li>Setting up the environment</li><li>The Pandas library</li><li>The Pandas DataFrame</li><li>The Pandas Series</li></ul><h4>Building an IMDB Top 250 Clone with Pandas</h4><ul><li>Technical requirements</li><li>The simple recommender</li><li>The knowledge-based recommender</li></ul><h4>Building Content-Based Recommenders</h4><ul><li>Technical requirements</li><li>Exporting the clean DataFrame</li><li>Document vectors</li><li>The cosine similarity score</li><li>Plot description-based recommender</li><li>Metadata-based recommender</li><li>Suggestions for improvements</li></ul><h4>Getting Started with Data Mining Techniques</h4><ul><li>Problem statement</li><li>Similarity measures</li><li>Clustering</li><li>Dimensionality reduction</li><li>Supervised learning</li><li>Evaluation metrics</li></ul><h4>Building Collaborative Filters</h4><ul><li>Technical requirements</li><li>The framework</li><li>User-based collaborative filtering</li><li>Item-based collaborative filtering</li><li>Model-based approaches</li></ul><h4>Hybrid Recommenders</h4><ul><li>Technical requirements</li><li>Introduction</li><li>Case study and final project ? Building a hybrid model</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Building Recommendation Systems with Python (TTAI2360) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Building Recommendation Systems with Python (TTAI2360) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Online Course Creation

Trainers Course

Building Recommendation Systems with Python (TTAI2360)

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Get ready for an exceptional online learning experience with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security</strong>bundle! This carefully curated collection of<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> 30 premium courses</strong> is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security is a dynamic package, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you&#39;re looking to break into a new field or deepen your existing knowledge, the<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security </strong>package has something for everyone.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">As part of the Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This Bundle Comprises the Following </strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security</strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPD Accredited Courses:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 01</strong>: Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 02</strong>: Exam Prep: AWS Certified Solutions Architect Associate 2021</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 03</strong>: Data Center Training Essentials: General Introduction</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 04</strong>: Data Center Training Essentials: Mechanical &amp; Cooling</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 05</strong>: Internet of Things</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 06</strong>: Web Application Penetration Testing Course</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 07</strong>: Google Cloud for Beginners</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 08</strong>: MySQL Database Development Mastery</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 09</strong>: Microsoft Azure Cloud Concepts</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 10</strong>: Azure Machine Learning</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 11</strong>: Cyber Security Incident Handling and Incident Response</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 12</strong>: IT Administration and Networking</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 13</strong>: CompTIA Network+ Certification (N10-007)</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 14</strong>: CompTIA CySA+ Cybersecurity Analyst (CS0-002)</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 15</strong>: Learn Ethical Hacking From A-Z: Beginner To Expert</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 16</strong>: CompTIA IT Fundamentals ITF+ (FCO-U61)</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 17</strong>: CISRM - Certified Information Systems Risk Manager</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 18</strong>: Quick Data Science Approach from Scratch</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 19</strong>: Project on Deep Learning - Artificial Neural Network</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 20</strong>: Deep Learning Neural Network with R</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 21</strong>: Cyber Security Awareness Training</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 22</strong>: CompTIA A+ (220-1001)</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 23</strong>: Computer Networks Security from Scratch to Advanced</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 24</strong>: Career Development Plan Fundamentals</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 25</strong>: CV Writing and Job Searching</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 26</strong>: Learn to Level Up Your Leadership</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 27</strong>: Networking Skills for Personal Success</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 28</strong>: Excel: Top 50 Microsoft Excel Formulas in 50 Minutes!</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 29</strong>: Decision Making and Critical Thinking</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 30</strong>: Time Management Training - Online Course</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What will make you stand out?</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Upon completion of this online Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security bundle, you will gain the following:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">CPD QS Accredited Proficiency with this <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security</strong> bundle</li><li fr-original-style="" style="box-sizing: border-box;">After successfully completing the Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security bundle, you will receive a FREE CPD PDF Certificates as evidence of your newly acquired abilities.</li><li fr-original-style="" style="box-sizing: border-box;">Lifetime access to the whole collection of learning materials of this <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security</strong> bundle</li><li fr-original-style="" style="box-sizing: border-box;">The online test with immediate results</li><li fr-original-style="" style="box-sizing: border-box;">You can study and complete the Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security bundle at your own pace.</li><li fr-original-style="" style="box-sizing: border-box;">Study for the Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Each course in this Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Embrace the future of learning with the Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security, a rich anthology of 30 diverse courses. Each course in the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security</strong> bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisCloud Infrastructure Engineer with CompTIA &amp; Cyber Security bundle will take you on a unique and enriching educational journey.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security bundle offers you the flexibility and convenience to learn at your own pace. Make the Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security package your trusted companion in your lifelong learning journey.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">CPD</div>
    </h2>

                        <div id="cpd" fr-original-style="" style="box-sizing: border-box;">
                            <div fr-original-style="" style="box-sizing: border-box;">
                                <div fr-original-style="" style="box-sizing: border-box;">
                                    <div fr-original-style="" style="box-sizing: border-box;">
                                        300 CPD hours / points
                                    </div>
                                        <div fr-original-style="" style="box-sizing: border-box;">
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security</strong> bundle is perfect for:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lifelong learners looking to expand their knowledge and skills.</li><li fr-original-style="" style="box-sizing: border-box;">Professionals seeking to enhance their career with CPD certification.</li><li fr-original-style="" style="box-sizing: border-box;">Individuals wanting to explore new fields and disciplines.</li><li fr-original-style="" style="box-sizing: border-box;">Anyone who values flexible, self-paced learning from the comfort of home.</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Requirements</div>
    </h2>

                        <div id="requirements" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You are cordially invited to enroll in this bundle; please note that there are no formal prerequisites or qualifications required. We&#39;ve designed this curriculum to be accessible to all, irrespective of prior experience or educational background.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
    </h2>

                        <div id="career_path" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Unleash your potential with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Cloud Infrastructure Engineer with CompTIA &amp; Cyber Security </strong>bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CompTIA &amp; Cyber Security</strong>bundle package.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
    </h2>

                        <div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">CPD Quality Standard Certificate</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>
            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">30 CPD Quality Standard Certificates - Free</p></div>

                        </div>

NextGen Learning

Certified in Cybersecurity (CC)

IT Certifications

Certified Cloud Security Professional (CCSP)

AWS Certified Cloud Practitioner

Certified Cyber Professional (CCP)

CompTIA Security+

Cloud Infrastructure Engineer with CompTIA &  Cyber Security- 30 CPD Certified Courses!

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Get ready for an exceptional online learning experience with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong> bundle! This carefully curated collection of<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> 30 premium courses</strong> is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The Mechanical And Mechatronics Engineering &amp; Energy Engineer  is a dynamic package, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you&#39;re looking to break into a new field or deepen your existing knowledge, the<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer  </strong>package has something for everyone.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">As part of the Mechanical And Mechatronics Engineering &amp; Energy Engineer  package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Mechanical And Mechatronics Engineering &amp; Energy Engineer  bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This Bundle Comprises the Following </strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPD Accredited Courses:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 01:</strong> Mechanical Engineering</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 02:</strong> Engineering Mechanics Course for Beginners</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 03:</strong> Crack Your Mechanical Engineer Interview</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 04:</strong> Automotive Engineering: Onboard Diagnostics</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 05:</strong> Automotive Design</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 06:</strong> Hybrid Vehicle Expert Training</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 07:</strong> Engine Lubrication Systems Online Course</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 08:</strong> Rotating Machines</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 09:</strong> Electric Circuits for Electrical Engineering</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 10:</strong> Electrical Machines for Electrical Engineering</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 11:</strong> Electronic &amp; Electrical Devices Maintenance &amp; Troubleshooting</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 12:</strong> Digital Electric Circuits &amp; Intelligent Electrical Devices</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 13:</strong> MATLAB Simulink for Electrical Power Engineering</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 14:</strong> Power Electronics for Electrical Engineering</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 15:</strong> Energy Engineer Course</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 16:</strong> Energy Saving in Electric Motors</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 17:</strong> Electric Vehicle Battery Management System</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 18:</strong> A complete course on Turbocharging</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 19:</strong> Heating Ventilation and AirConditioning (HVAC) Technician</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 20:</strong> Python Intermediate Training</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 21:</strong> Spatial Data Visualisation and Machine Learning in Python</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 22:</strong> Data Center Training Essentials: Mechanical &amp; Cooling</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 23:</strong> Career Development Plan Fundamentals</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 24:</strong> CV Writing and Job Searching</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 25: </strong>Learn to Level Up Your Leadership</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 26:</strong> Networking Skills for Personal Success</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 27:</strong> Ace Your Presentations: Public Speaking Masterclass</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 28:</strong> Learn to Make a Fresh Start in Your Life</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 29:</strong> Motivation - Motivating Yourself &amp; Others</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 30:</strong> Excel: Top 50 Microsoft Excel Formulas in 50 Minutes!</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What will make you stand out?</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Upon completion of this online <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong>  bundle, you will gain the following:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">CPD QS Accredited Proficiency with this  Mechanical And Mechatronics Engineering &amp; Energy Engineer  </li><li fr-original-style="" style="box-sizing: border-box;">After successfully completing the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong>&nbsp; bundle, you will receive a FREE CPD PDF Certificates as evidence of your newly acquired abilities.</li><li fr-original-style="" style="box-sizing: border-box;">Lifetime access to the whole collection of learning materials of this Mechanical And Mechatronics Engineering &amp; Energy Engineer .</li><li fr-original-style="" style="box-sizing: border-box;">The online test with immediate results </li><li fr-original-style="" style="box-sizing: border-box;">You can study and complete the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical And Mechatronics Engineering &amp; Energy Engineer </strong> bundle at your own pace.</li><li fr-original-style="" style="box-sizing: border-box;">Study for the Mechanical And Mechatronics Engineering &amp; Energy Engineer  bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Each course in this <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer  </strong>bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Embrace the future of learning with the Mechanical And Mechatronics Engineering &amp; Energy Engineer, a rich anthology of 30 diverse courses. Each course in the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong> bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisMechanical And Mechatronics Engineering &amp; Energy Engineer  bundle will take you on a unique and enriching educational journey.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Mechanical And Mechatronics Engineering &amp; Energy Engineer  bundle offers you the flexibility and convenience to learn at your own pace. Make the Mechanical And Mechatronics Engineering &amp; Energy Engineer  package your trusted companion in your lifelong learning journey.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">CPD</div>
    </h2>

                        <div id="cpd" fr-original-style="" style="box-sizing: border-box;">
                            <div fr-original-style="" style="box-sizing: border-box;">
                                <div fr-original-style="" style="box-sizing: border-box;">
                                    <div fr-original-style="" style="box-sizing: border-box;">
                                        300 CPD hours / points
                                    </div>
                                        <div fr-original-style="" style="box-sizing: border-box;">
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong> bundle is perfect for:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lifelong learners looking to expand their knowledge and skills.</li><li fr-original-style="" style="box-sizing: border-box;">Professionals seeking to enhance their career with CPD certification.</li><li fr-original-style="" style="box-sizing: border-box;">Individuals wanting to explore new fields and disciplines.</li><li fr-original-style="" style="box-sizing: border-box;">Anyone who values flexible, self-paced learning from the comfort of home.</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Requirements</div>
    </h2>

                        <div id="requirements" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You are cordially invited to enroll in this bundle; please note that there are no formal prerequisites or qualifications required. We&#39;ve designed this curriculum to be accessible to all, irrespective of prior experience or educational background.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
    </h2>

                        <div id="career_path" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Unleash your potential with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer  </strong>bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with thisbundle package.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
    </h2>

                        <div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">CPD Quality Standard Certificate</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>
            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">30 CPD Accredited Digital Certificates and A Hard Copy Certificate </p></div>

                        </div>

Mechatronics

Mechanical Engineering

Engineering Construction Industry Training (ECITB)

Mechanical Engineer

Institution of Mechanical Engineers (IMechE)

Engineering Management

Mechanical Engineering, Mechatronics Engineering & Energy Engineer - 30 Courses Bundle

<strong>In this course, you will be learning one of the widely used deep learning frameworks, that is, PyTorch, and learn the basics of convolutional neural networks in PyTorch. We will also cover the basics of Python and understand how to implement different Python libraries.</strong>
        <p>Note: The course is primarily focused on teaching PyTorch and deep learning for computer vision, but it also includes a few sections on the fundamentals of Python (Sections 8-12). These optional learning sections are designed for individuals who may be new to Python or who want to refresh their knowledge of Python basics.

In this course, we will take a step-by-step method by first grasping PyTorch's fundamentals. Then, using a guide to getting free GPU for learning, you will learn how to code in GPU. You will then learn about PyTorch's AutoGrad feature and how to use it. Later, you will learn how to use PyTorch to create deep learning models and understand the fundamentals of convolutional neural networks (CNN). You will also learn how to use CNN with a real-world dataset.

Additionally, the course will emphasize the fundamentals and lay the groundwork for an understanding of Python. We will also talk about the three significant Python libraries known as NumPy, Pandas, and Matplotlib. In this part of the course, we will also build a mini project where we will be building a hangman game in Python.

By the end of this course, we will be able to perform Computer Vision tasks with deep learning.

All the resources for this course are available at:  https://github.com/PacktPublishing/Deep-Learning---Computer-Vision-for-Beginners-Using-PyTorch</p>
        <h2>What You Will Learn</h2><p>Learn how to work with PyTorch<br>Build intuition on convolution operation on images<br>Implement gradient descent using AutoGrad<br>Learn about LeNet architecture<br>Create a mini-Python project game<br>Understand how to use NumPy, Pandas, and Matplotlib libraries</p>
        <h2>Audience</h2><p>Software developers, machine learning practitioners, data scientists, and anybody else interested in understanding PyTorch and deep learning should take this course. While a basic knowledge of Python would be beneficial, it is not a prerequisite as we will be covering the necessary fundamentals during the course.</p>
        <h2>Approach</h2><p>This is a step-by-step learning course where we will start with the basics and move toward real-world implementation. You will also learn the basics of Python programming and build a mini project to test our learning in the real world.</p>
        <h2>Key Features</h2><p>Learn how to perform Computer Vision tasks with deep learning * Learn to implement LeNet architecture on CIFAR10 dataset, which has 60,000 images * Build your programming foundation with Python</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Deep-Learning---Computer-Vision-for-Beginners-Using-PyTorch</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 Aboard</h4><p>Welcome to the course! This is a quick introductory section.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>In this video, we will have a quick course introduction.</p></td></tr><tr><td style='padding: 15px;'><p>2. Why Is PyTorch Powerful?</p><p style='margin: 0'>In this video, we will look at a quick demo and understand why PyTorch is powerful.</p></td></tr></tbody></table><br><h4>2. Introduction to PyTorch and Tensors</h4><p>In this section, we will get introduced to PyTorch and Tensors.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is PyTorch</p><p style='margin: 0'>In this video, we will get a brief introduction to PyTorch.</p></td></tr></tbody></table><br><h4>3. Diving into PyTorch</h4><p>In this section, we will dive into PyTorch and get in action.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Installing PyTorch</p><p style='margin: 0'>In this video, you will learn how to install PyTorch using Google Colab ipynb.</p></td></tr><tr><td style='padding: 15px;'><p>2. Create Tensors in PyTorch</p><p style='margin: 0'>In this video, you will learn how to create Tensors in PyTorch.</p></td></tr><tr><td style='padding: 15px;'><p>3. Tensor Slicing and Reshape</p><p style='margin: 0'>In this video, you will learn how to reshape and slice a Tensor.</p></td></tr><tr><td style='padding: 15px;'><p>4. Mathematical Operations on Tensors</p><p style='margin: 0'>In this video, let's see how we can perform mathematical operations on Tensors.</p></td></tr><tr><td style='padding: 15px;'><p>5. NumPy in PyTorch</p><p style='margin: 0'>In this video, you will learn how to convert a NumPy array in PyTorch.</p></td></tr><tr><td style='padding: 15px;'><p>6. What Is CUDA</p><p style='margin: 0'>In this video, we will first understand what CUDA is and then see it in action.</p></td></tr><tr><td style='padding: 15px;'><p>7. PyTorch on GPU</p><p style='margin: 0'>In this video, we will test the competition speed with GPU.</p></td></tr></tbody></table><br><h4>4. AutoGrad in PyTorch</h4><p>In this section, we will have a look at AutoGrad in PyTorch.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. AutoGrad in PyTorch</p><p style='margin: 0'>In this video, you will learn what is AutoGrad in PyTorch.</p></td></tr><tr><td style='padding: 15px;'><p>2. AutoGrad in a Loop</p><p style='margin: 0'>In this video, you will learn to implement the AutoGrad function in a loop.</p></td></tr></tbody></table><br><h4>5. Creating Deep Neural Networks in PyTorch</h4><p>In this section, we will be working on creating a deep neural network in PyTorch.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Building the First Neural Network</p><p style='margin: 0'>In this video, you will learn how to build your first neural network.</p></td></tr><tr><td style='padding: 15px;'><p>2. Writing a Deep Neural Network</p><p style='margin: 0'>In this video, you will learn how to write a deep neural network.</p></td></tr><tr><td style='padding: 15px;'><p>3. Writing a Custom NN Module</p><p style='margin: 0'>In this video, you will learn how to write a custom NN (Neural Network) module.</p></td></tr></tbody></table><br><h4>6. CNN in PyTorch</h4><p>In this section, you will learn how to implement a convolutional neural network in PyTorch.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Loading - CIFAR10</p><p style='margin: 0'>In this video, you will learn how to load our CIFAR10 dataset in PyTorch.</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Visualization</p><p style='margin: 0'>In this video, you will learn how to visualize your data to understand it better.</p></td></tr><tr><td style='padding: 15px;'><p>3. CNN Recap</p><p style='margin: 0'>In this video, we will have a quick recap on the convolution operation.</p></td></tr><tr><td style='padding: 15px;'><p>4. First CNN</p><p style='margin: 0'>In this video, we will work on building our first convolution layer.</p></td></tr><tr><td style='padding: 15px;'><p>5. CNN Deep Layers</p><p style='margin: 0'>In this video, you will learn how to perform a series of convolution operations to the required output.</p></td></tr></tbody></table><br><h4>7. LeNet Architecture in PyTorch</h4><p>In this section, you will learn how to implement the LeNet architecture in PyTorch.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. LeNet Overview</p><p style='margin: 0'>In this video, let's first understand what LeNet architecture is.</p></td></tr><tr><td style='padding: 15px;'><p>2. LeNet Model in PyTorch</p><p style='margin: 0'>In this video, you will learn how to implement the LeNet model in PyTorch.</p></td></tr><tr><td style='padding: 15px;'><p>3. Preparation and Evaluation</p><p style='margin: 0'>In this video, you will learn how to train our LeNet model.</p></td></tr></tbody></table><br><h4>8. Optional Learning- Python Basics</h4><p>In this section, you will be learning the basics of Python.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Why Learn Any Programming Language</p><p style='margin: 0'>In this video, we will first understand why we should learn any programming language.</p></td></tr><tr><td style='padding: 15px;'><p>2. Why Choose Python</p><p style='margin: 0'>In this video, we will understand the benefits of Python.</p></td></tr><tr><td style='padding: 15px;'><p>3. Installing Jupyter Notebook</p><p style='margin: 0'>In this video, you will learn how to install Jupyter Notebook.</p></td></tr><tr><td style='padding: 15px;'><p>4. Jupyter Notebook - Tips and Tricks</p><p style='margin: 0'>In this video, you will learn some useful tips and tricks of working in Jupyter Notebook.</p></td></tr><tr><td style='padding: 15px;'><p>5. What We Will Cover in This Section</p><p style='margin: 0'>In this video, we will understand the learning objective of the Python basics section.</p></td></tr><tr><td style='padding: 15px;'><p>6. Variables in Python</p><p style='margin: 0'>In this video, you will learn about variables in Python.</p></td></tr><tr><td style='padding: 15px;'><p>7. Print Function</p><p style='margin: 0'>In this video, you will learn about the Print function.</p></td></tr><tr><td style='padding: 15px;'><p>8. Numerical Data Types and Arithmetic Operations in Python</p><p style='margin: 0'>In this video, you will learn about numerical data types and arithmetic operations in Python.</p></td></tr><tr><td style='padding: 15px;'><p>9. String Data Type</p><p style='margin: 0'>In this video, you will learn about the string data type.</p></td></tr><tr><td style='padding: 15px;'><p>10. Boolean Data Type</p><p style='margin: 0'>In this video, we will understand the Boolean data type.</p></td></tr><tr><td style='padding: 15px;'><p>11. Type Conversion and Type Casting</p><p style='margin: 0'>In this video, we will understand how to convert one data type to another data type.</p></td></tr><tr><td style='padding: 15px;'><p>12. Adding Comments in Python Programming Language</p><p style='margin: 0'>In this video, you will learn how to add comments to our programs.</p></td></tr><tr><td style='padding: 15px;'><p>13. Data Structures in Python</p><p style='margin: 0'>In this video, we will understand the data structures in Python.</p></td></tr><tr><td style='padding: 15px;'><p>14. Tuples and Sets in Python</p><p style='margin: 0'>In this video, you will learn about tuples and sets in Python.</p></td></tr><tr><td style='padding: 15px;'><p>15. Python Dictionaries</p><p style='margin: 0'>In this video, you will learn about Python dictionaries.</p></td></tr><tr><td style='padding: 15px;'><p>16. Conditional Statements in Python - if</p><p style='margin: 0'>In this video, you will learn about the "If" conditional statement and how to implement it.</p></td></tr><tr><td style='padding: 15px;'><p>17. Conditional Statements in Python - While</p><p style='margin: 0'>In this video, you will learn about the "While" conditional statement and how to implement it.</p></td></tr><tr><td style='padding: 15px;'><p>18. Inbuilt Functions in Python - range and input</p><p style='margin: 0'>In this video, you will learn two important inbuilt functions in Python, which are range and input functions.</p></td></tr><tr><td style='padding: 15px;'><p>19. For Loops</p><p style='margin: 0'>In this video, you will learn about for loops.</p></td></tr><tr><td style='padding: 15px;'><p>20. Functions in Python</p><p style='margin: 0'>In this video, you will learn about functions in Python.</p></td></tr><tr><td style='padding: 15px;'><p>21. Classes in Python</p><p style='margin: 0'>In this video, you will learn about classes in Python.</p></td></tr></tbody></table><br><h4>9. Optional Learning - Mini Project with Python Basics</h4><p>In this section, we will be working on a mini project where we will implement our learning from the Python basics section.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Mini Project - Hangman</p><p style='margin: 0'>In this video, we will get introduced to our mini project, which is called the Hangman.</p></td></tr><tr><td style='padding: 15px;'><p>2. Writing a Class</p><p style='margin: 0'>In this video, we will start writing the program for our classes and objects.</p></td></tr><tr><td style='padding: 15px;'><p>3. Mini Project - Continued</p><p style='margin: 0'>In this video, we will continue writing the program for our hangman game.</p></td></tr><tr><td style='padding: 15px;'><p>4. Logic Building</p><p style='margin: 0'>In this video, we will work on building the logic.</p></td></tr><tr><td style='padding: 15px;'><p>5. Logic for Single-Letter input</p><p style='margin: 0'>In this video, we will continue building the logic for single-letter input.</p></td></tr><tr><td style='padding: 15px;'><p>6. Final Testing</p><p style='margin: 0'>In this video, we will run our project and check whether our program runs as required.</p></td></tr></tbody></table><br><h4>10. Optional Learning - Python for Data Science with NumPy</h4><p>IN this section, we will understand how to work with the NumPy library.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. NumPy</p><p style='margin: 0'>In this video, we will first understand how to create arrays using NumPy library.</p></td></tr><tr><td style='padding: 15px;'><p>2. Resize and Reshape Arrays</p><p style='margin: 0'>In this video, you will learn how to resize and reshape an array.</p></td></tr><tr><td style='padding: 15px;'><p>3. Slicing</p><p style='margin: 0'>In this video, we will understand how to perform slicing on NumPy arrays.</p></td></tr><tr><td style='padding: 15px;'><p>4. Broadcasting</p><p style='margin: 0'>In this video, we will understand the concept of broadcasting.</p></td></tr><tr><td style='padding: 15px;'><p>5. Mathematical Operations and Functions in NumPy</p><p style='margin: 0'>In this video, we will understand the different mathematical operations and functions that we can perform on NumPy arrays.</p></td></tr></tbody></table><br><h4>11. Optional Learning - Python for Data Science with Pandas</h4><p>In this section, you will learn about Pandas.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Pandas Library</p><p style='margin: 0'>In this video, you will learn about the Pandas library.</p></td></tr><tr><td style='padding: 15px;'><p>2. Pandas Dataframe</p><p style='margin: 0'>In this video, you will learn about Pandas Dataframe.</p></td></tr><tr><td style='padding: 15px;'><p>3. Pandas Dataframe - Load from External File</p><p style='margin: 0'>In this video, you will learn how to load the data to our dataframe from an external file.</p></td></tr><tr><td style='padding: 15px;'><p>4. Working with Null Values</p><p style='margin: 0'>In this video, you will learn how to work with null values.</p></td></tr><tr><td style='padding: 15px;'><p>5. Slicing Pandas Dataframe</p><p style='margin: 0'>In this video, we will understand how we can return the required elements from our dataframe using the concept of slicing.</p></td></tr><tr><td style='padding: 15px;'><p>6. Imputation</p><p style='margin: 0'>In this video, we will understand how to perform imputation on our dataframe.</p></td></tr></tbody></table><br><h4>12. Optional Learning - Python for Data Science with Matplotlib</h4><p>In this section, we will talk about Matplotlib.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Matplotlib Introduction</p><p style='margin: 0'>In this video, we will get introduced to Matplotlib.</p></td></tr><tr><td style='padding: 15px;'><p>2. Format the Plot</p><p style='margin: 0'>In this video, you will learn how to format our Matplotlib plot.</p></td></tr><tr><td style='padding: 15px;'><p>3. Plot Formatting and Scatter Plot</p><p style='margin: 0'>In this video, we will cover plot formatting and scatter plot.</p></td></tr><tr><td style='padding: 15px;'><p>4. Histplot</p><p style='margin: 0'>In this video, you will learn how to create a Histplot.</p></td></tr></tbody></table>

Computer Vision

Pandas

In this course, you will be learning one of the widely used deep learning frameworks, that is, PyTorch, and learn the basics of convolutional neural networks in PyTorch. We will also cover the basics of Python and understand how to implement different Python libraries.

Deep Learning - Computer Vision for Beginners Using PyTorch

<strong>This course does not require any prior knowledge of Apache Spark or Hadoop. The author explains Spark architecture and fundamental concepts to help you come up to speed and grasp the content of this course.

The course will help you understand Spark programming and apply that knowledge to build data engineering solutions.</strong>
        <p>Apache Spark is a lightning-fast unified analytics engine for big data and machine learning. Since its release, Apache Spark has seen rapid adoption by enterprises across a wide range of industries. Internet powerhouses such as Netflix, Yahoo, and eBay have deployed Spark at a massive scale. It has quickly become the largest open-source community in big data. So, mastering Apache Spark opens a wide range of professional opportunities.

This course starts with an introduction to Apache Spark where you see what Apache Spark is in brief. Then, you will be installing and using Apache Spark. After that, you will look at the Spark execution model and architecture in detail. Next, you will learn the Spark programming model and developer experience. Following that, you will look at the Spark Structured API foundation, and Spark data sources and sinks. Then, you will explore Spark Data frame and dataset transformations along with aggregations in Apache Spark. Finally, you will look at the Spark Data frame joins in detail.

By the end of this course, you will understand Spark programming and apply that knowledge to build data engineering solutions.

All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Spark-Programming-in-Scala-for-Beginners-with-Apache-Spark-3</p>
        <h2>What You Will Learn</h2><p>Learn Apache Spark and Spark architecture<br>Look at data engineering and data processing in Spark<br>Work with data sources and sinks<br>Work with data frames, datasets, and Spark SQL<br>Use IntelliJ IDEA for Spark development and debugging<br>Understand unit testing, managing application logs, and cluster deployment</p>
        <h2>Audience</h2><p>This course is designed for software engineers willing to develop a data engineering pipeline and application using Apache Spark. It is also for data architects and data engineers who are responsible for designing and building the organization's data-centric infrastructure. It will also be beneficial for the managers and architects who do not directly work with Spark implementation, and still, they work with the people who implement Apache Spark at the ground level.<br><br>Before proceeding with the course, you will need basic knowledge of the Scala programming language.</p>
        <h2>Approach</h2><p>This is a hands-on and well-balanced course with theoretical and practical content. The course is example-driven and follows a working session-like approach. The author will be taking a live coding approach and explaining all the concepts needed along the way. The section will have a summary video at the end to revise the learned concepts.</p>
        <h2>Key Features</h2><p>A comprehensive course designed for the beginner-level for Spark programming in Scala * Deep dive into Spark 3 architecture and data engineering * Complete tested source code and examples used on Apache Spark 3.0.0 open-source distribution from the author's end</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Spark-Programming-in-Scala-for-Beginners-with-Apache-Spark-3</p>
        <h2>About the Author</h2><h3>Scholar Nest</h3><p>ScholarNest is a small team of people passionate about helping others learn and grow in their careers by bridging the gap between their existing and required skills.

Together, they have over 40+ years of experience in IT as a developer, architect, consultant, trainer, and mentor. They have worked with international software services organizations on various data-centric and Big Data projects.

It is a team of firm believers in lifelong continuous learning and skill development. To popularize the importance of continuous learning, they started publishing free training videos on their YouTube channel. They conceptualized the notion of continuous learning, creating a journal of our learning under the Learning Journal banner.</p>
        <h2>Course Outline</h2><h4>1. Apache Spark Introduction</h4><p></p><br><h4>2. Installing and Using Apache Spark</h4><p></p><br><h4>3. Spark Execution Model and Architecture</h4><p></p><br><h4>4. Spark Programming Model and Developer Experience</h4><p></p><br><h4>5. Spark Structured API Foundation</h4><p></p><br><h4>6. Spark Data Sources and Sinks</h4><p></p><br><h4>7. Spark Dataframe and Dataset Transformations</h4><p></p><br><h4>8. Aggregations in Apache Spark</h4><p></p><br><h4>9. Spark Dataframe Joins</h4><p></p><br><h4>10. Keep Learning</h4><p></p>

Scala

Apache Spark

Hadoop

Big Data

This course does not require any prior knowledge of Apache Spark or Hadoop. The author explains Spark architecture and fundamental concepts to help you come up to speed and grasp the content of this course.

The course will help you understand Spark programming and apply that knowledge to build data engineering solutions.

Spark Programming in Scala for Beginners with Apache Spark 3

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			<p>The course is designed for Google Ads (Google AdWords) practitioners that already have basic knowledge in setting up and running AdWords accounts.  The course explores advanced techniques and ideas that I have picked up and developed over 10 years running AdWords campaigns for small to large scale clients.</p>
<p>I explain the idea behind each technique and I give you ideas and example cases on how I have used it.  I then show you how to set it up step by step inside Google Ads.  We also look at how to interpret results and how to apply it to your own campaigns, we look at real data and actual results.</p>
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<h3><strong>What you'll learn</strong></h3>
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<li><span>Google Ads Campaign Experiments: Best Use Cases, Step-by-Step Setup, Analysis, Implementation</span></li>
<li>Access Google Ads tools for forecasting performance</li>
<li>Budget Optimisation techniques using regression formula</li>
<li><span>Advanced Concept Testing: moving beyond A/B ad testing to testing concepts</span></li>
<li><span>Improving Geotargeting for Local Businesses With A Double Campaign Structure</span></li>
<li><span>How To Setup, Run, Pivot &amp; Interpret the Paid &amp; Organic Report</span></li>
<li><span>Make A Strong Argument For Running Branded Ads Using the Paid &amp; Organic Report</span></li>
<li><span>Identify Opportunities Between Organic &amp; Paid Listings</span></li>
<li><span>Learn to use a free Google Sheets template for automating and tracking budgets in multiple ad accounts</span></li>
<li><span>How to find the key pitfalls in any Google Ads account</span></li>
<li><span>How to Audit a new or existing Google Ads account and find issues to improve upon quickly</span></li>
<li><span>Utilise Dynamic Ad Customisers for more personalised ads</span></li>
<li><span>Understand the power of using Google Analytics with Google Ads and be familiar with Audience setup and relevant Google Ads reports in GA</span></li>
<li><span>Understand attribution models and why the default last click is not always ideal</span></li>
<li><span>Implement Machine Learning features within Google Ads to benefit your account in ways that human optimisation could never reach</span></li>
<li><span>Develop a multi-funnel remarketing strategy for GDN and search campaign</span></li>
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<h3><strong>Requirements</strong></h3>
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<li>Able to set up your own Google Ads account</li>
<li>Be confident navigating Google Ads accounts</li>
<li>Basic exposure to excel filtering &amp; pivot tables</li>
</ul>
</div>
</div>
</div>
</div>
</div><table>				<tbody><tr>
					<td colspan="4">Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>1. Introduction</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Testing to Ad Concepts </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>2. Introduction to Ad Concepts</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>3.Example Ad Concepts</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>4. Label Setup</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>5. Reporting on Ad Concepts</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Google Ads Paid % Organic Interaction Report</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>6. Understanding the usefulness of this report</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>7.Linking Google Ads with Search Console</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>8.Excel Report Setup &amp; Pivot</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>9.Identifying Google Ads &amp; SEO Search Term Opportunities</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>10.CTR Interaction between SEO &amp; SEM</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Geolocal Campaigns - Double Campaign Structure</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>11.The theory behind the double campaign</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>12.A practical example inside Google Ads</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Google Ads Campaign - Experiments (Drafts &amp; Experiments) </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>13.Introduction to Drafts &amp; Experiments</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>14.Example experiment Ideas for use</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>15.Draft &amp; Experiment Setup</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>16.Report Analysis &amp; explanation of symbols</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Automate Budgeting for Multiple Ad Accounts</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>17. Introduction to the Google Spreedsheet template</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>18. Data Inputs</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>19. Reading the sheet output</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">AdWords Audit</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>20. Audit Overview</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>21. Auditing the Account Structure</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>22. Campaign Settings</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>23. Auditing on the AdGroup Level</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>24. Other Things to look for in an Audit</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Remarketing Strategy &amp; Implementation</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>25. Why Remarketing Works</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>26.GDN Remarketing</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>27.Remarketing Lists for Search Ads (RLSA) &amp; IF Statements</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>28. Customer Match (Email Retargeting)</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>29. Dynamic Remarketing</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>30. Lookalike Audiences</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>31. GDN Banner Sizes &amp; Other Things to Consider</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>32. Audience List Sharing</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>33. Remarketing with Events</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Artificial Intelligence &amp; Machine Learning </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>34.What is AI &amp; ML</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>35. Auto Rotate for Budgets &amp; Ads</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>36.Dynamic Search Ads</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>37.Smart Bidding Strategies</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>38.Lookalike Audiences</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>39.Data Driven Attribution Models</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Dynamic Ad Customisers </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>40.Introduction to Ad Customisers</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>41. Advantages &amp; Example Uses</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>42.Setting up the Business Data Feed</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>43.Best Practice for Ads</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Google Analytics for Google Ads</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>44. The Advantages of linking AdWords with GA</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>45. Linking GA &amp; AdWords</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>46. AdWords Reports in GA</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>47. GA Conversion Tracking &amp; Goal Setup</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>48. Remarketing Segments in GA</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>49. Attribution Models in Google Analytics</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Using Scripts</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>50. Introduction to AdWords Scripts</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>51. 404 Link Checker Script</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>52. Experiment Results Script</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>53. Google Ads Audit Script</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">AdWords Editor </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>54. Introduction and advantages of AdWords Editor over the online interface</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>55. Example Uses for AdWords Editor</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>56.AdWords Editor - what you can't do</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Competitor Research Tools</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>57.Auction Insights displayed graphically</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>58.3rd Party Competitor Research Tools -SEMrush</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Landing Page Software</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>59.Why Use Landing Page Software</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>60.Unbounce Walk-through</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>61. Tracking integration for landing pages</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">New Features in 2018/19</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>62.Ad Variations</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>63.Search Responsive Ads</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>64. Life Events Audiences</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>65. Custom Intent Audiences</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
				</tbody></table>

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Advanced Google Ads / AdWords Training - Updated for 2019

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is geared for experienced skilled Java developers, software developers, data scientists, machine learning experts or others who wish to transtion their coding skills to Scala, learning how to code in Scala and apply it in a practical way. This is not a basic class.</p>Overview<p>Working in a hands-on learning environment led by our expert instructor you'll:<br />Get comfortable with Scala's core principles and unique features, helping you navigate the language confidently and boosting your programming skills.<br />Discover the power of functional programming and learn techniques that will make your code more efficient,maintainable, and enjoyable to write.<br />Become proficient in creating dynamic web applications using the Play Framework, and easily connect to databases with the user-friendly Slick library.<br />Master concurrency programming with Akka, empowering you to build scalable and fault-tolerant applications that excel in performance.<br />Enhance your testing skills using ScalaTest and ScalaCheck, ensuring the reliability and quality of your Scala applications, while having fun in the process.<br />Explore the fascinating world of generative AI and GPT technologies, and learn how to integrate them into your projects,adding a touch of innovation and intelligence to your Scala solutions.<br />If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals.</p><p>Discover the power of Scala programming in our comprehensive, hands-on technical training course designed specifically for experienced object-oriented (OO) developers. Scala is a versatile programming language that combines the best of both OO and functional programming paradigms, making it ideal for a wide range of projects, from web applications to big data processing and machine learning. By mastering Scala, you'll be able to develop more efficient, scalable, and maintainable applications. Fast Track to Scala Programming for OO / Java Developers is a four day hands-on course covers the core principles of Scala, functional programming, web application development, database connectivity, concurrency programming, testing, and interoperability between Scala and Java. Additionally, you'll explore cutting-edge generative AI and GPT technologies, learning how to integrate them into your Scala applications for intelligent suggestions or automation. Throughout the course you?ll explore the latest tools and best practices in the Scala ecosystem, gaining valuable knowledge and experience that can be directly applied to your day-to-day work. With 50% of the course content dedicated to hands-on labs, you'll gain practical experience applying the concepts you've learned across various projects, such as building functional web applications, connecting to databases, designing modular components, and implementing concurrency. Upon completing the course, you'll have a solid understanding of the language and its features, empowering you to confidently apply your new skills in data science and machine learning projects. You'll exit well-prepared to create efficient, scalable, and maintainable Scala applications, regardless of the complexity of your projects.</p></div><div id="course-pills-2"><h4>Introduction to Scala</h4><ul><li>Scala features and benefits</li><li>Comparing Scala with Java and other OO languages</li><li>Installing Scala and setting up the development environment</li></ul><h4>Object-Oriented Programming in Scala</h4><ul><li>Classes and objects</li><li>Traits, mixins, and inheritance</li><li>Companion objects and factories</li><li>Encapsulation and polymorphism</li></ul><h4>Functional Programming Basics</h4><ul><li>Pure functions and referential transparency</li><li>Higher-order functions and currying</li><li>Immutability and persistent data structures</li><li>Pattern matching and recursion</li></ul><h4>Having Fun with Functional Data Structures</h4><ul><li>Lists, sets, and maps in Scala</li><li>Folding and reducing operations</li><li>Stream processing and lazy evaluation For-comprehensions</li></ul><h4>Building Web Applications in Functional Style</h4><ul><li>Introduction to Play Framework</li><li>Functional web routing and request handling</li><li>JSON handling with Play-JSON</li><li>Middleware and functional composition</li></ul><h4>Connecting to a Database</h4><ul><li>Introduction to Slick library</li><li>Database configuration and setup</li><li>Querying and updating with Slick</li><li>Transactions and error handling</li></ul><h4>Building Scalable and Extensible Components </h4><ul><li>Modular architecture and design patterns<br />Dependency injection with MacWire<br />Type classes and type-level programming<br />Implicit parameters and conversions</li></ul><h4>Concurrency Programming &amp; Akka</h4><ul><li>Introduction to Akka framework and Actor model</li><li>Actor systems and message passing</li><li>Futures and Promises</li><li>Supervision and fault tolerance</li><li></li></ul><h4>Building Confidence with Testing</h4><ul><li>Introduction to ScalaTest and ScalaCheck</li><li>Unit testing and property-based testing</li><li>Test-driven development in Scala</li><li>Mocking and integration testing</li><li></li></ul><h4>Interoperability between Scala and Java</h4><ul><li>Calling Java code from Scala</li><li>Using Java libraries in Scala projects</li><li>Converting Java collections to Scala collections</li><li>Writing Scala code that can be called from Java</li></ul><h4>Using Generative AI and GPT</h4><ul><li>Technologies in Scala Programming</li><li>Overview of GPT and generative AI</li><li>Integrating GPT with Scala applications</li><li>Use cases and practical examples</li></ul></div>

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Fast Track to Scala Programming for OO / Java Developers (TTSCL2104)

<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>

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

Data Science

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>

Deep Learning

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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