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Practical Approach to Recommender Systems

Full Course Description:

Have you ever wondered how content on YouTube is tailored to your preferences? Or why Netflix recommends your favorite TV shows? If you’ve ever wanted to build your own personalized recommender system, then this is the course for you. Unlike other courses, this comprehensive package is designed for beginners and covers the basics of recommender systems, their applications, and how to build them from scratch using machine learning and deep learning with Python. Each module includes engaging content that combines practical examples with theoretical concepts. At the end of each module, you will have the opportunity to test your knowledge with a quiz. The solutions to the quizzes are provided in the following video. We will start by introducing the theoretical concepts of recommender systems and then dive into the important taxonomies that form the basis of these systems. By the end of the course, you will have a solid understanding of how to develop recommender systems using machine learning and deep learning with Python. Python is the most popular programming language for machine learning, and we’ll cover everything from beginner to advanced levels. Throughout the course, you’ll learn how to evaluate recommender system datasets based on user ratings, preferences, music genres, movie categories, and release years. You’ll also gain hands-on experience building content-based filtering and collaborative filtering techniques. The course includes several projects to enhance your learning experience. Machine learning is a highly sought-after skill, with machine learning engineers earning an average salary of over $110,000 in the United States. This course is suitable for beginners with some programming experience, as well as those new to data analytics, machine learning, and deep learning. It covers the same content as other expensive recommender systems courses, but at a fraction of the cost. With over 6 hours of high-definition video lectures and detailed code notebooks, this is one of the most comprehensive recommender systems courses using machine learning and deep learning available. Enroll today and become a recommender systems expert!

Why should you enroll in this course?

This course is designed to provide a unique hands-on experience in developing full-fledged recommendation system engines for custom datasets. It offers a hands-on, learn-by-doing approach that will help you master Python concepts and methodology. The course is easy to understand, expressive, and self-explanatory. It focuses on hands-on coding and includes three detailed projects that cover the entire course content. The material is comprehensive and covers the most advanced machine learning models discovered by renowned data scientists and AI practitioners. Our passion for teaching is evident in the high-quality video content, course notes, supplementary materials, and assessment exercises provided. Our friendly team is also available to answer any questions you may have.

Course Content:

In this course, you will learn how to program with Python and use machine learning concepts to develop recommendation systems. Here are just a few of the topics covered: 1. Course Overview 2. Motivation for Recommender Systems - Process of Recommender Systems - Goals of Recommender Systems - Generations of Recommender Systems - Nexus of Recommender Systems with Artificial Intelligence - Real-World Challenges of Recommender Systems - Applications of Recommender Systems Recommendation Systems 3. Basics of Recommender Systems - Taxonomy of Recommender Systems - Item-Context Matrix - User Rating Matrix - Inferring Preferences - Quality of Recommender Systems - Online and Offline Evaluation Techniques - Dataset Partitioning - Overfitting - Error Matrix - Content-Based Filtering - Collaborative Filtering - User-Based and Item-Based Collaborative Filtering 4. Machine Learning-Based Recommender Systems - Machine Learning in Recommender Systems - Benefits of Machine Learning in Recommender Systems - Design Approaches for Recommender Systems using Machine Learning - Guidelines for Machine Learning-Based Recommender Systems - Practical Approach for Content-Based Filtering using Machine Learning - Practical Approach for Item-Based Collaborative Filtering using Machine Learning Machine Learning 5. Project 1: Music Recommendation System for a Music App using Machine Learning 6. Project 2: Movie Recommendation System using K-Nearest Neighbors Algorithm 7. Deep Learning for Recommendation Systems - Overview of Deep Learning in Recommendation Systems - Benefits and Challenges of Deep Learning in Recommendation Systems - Deep Learning for Recommendation Inference - A Generic Recommendation Approach based on Deep Learning - Neutral Collaborative Filtering 8. Project: Amazon Product Recommendation System - Installing Packages - Analyzing Data for Product Recommendation - Preparing Data - Building the Model Using the Two-Tower Approach - Implementing TensorFlow Recommenders - Tuning and Evaluating the Recommendation System - Validating the Recommendation System - Testing a Recommendation Model - Making Predictions Using Recommender Systems

What you will learn:

Upon completion of this course, you will be able to: - Understand the basic concepts of recommender systems - Recognize the impact of AI-integrated recommender systems - Identify the key challenges and applications of recommender systems - Familiarize yourself with the taxonomy of recommender systems - Understand the concepts of overfitting, underfitting, bias, and variance - Gain a solid understanding of content-based filtering and collaborative filtering - Develop recommender systems using machine learning topologies with Python - Build recommender systems for various applications, such as Spotify music recommendations, using machine learning and Python - Gain hands-on experience in building content-based and item-based recommender systems using machine learning techniques and Python - Model k-nearest neighbors-based recommendation engines for different types of recommender system applications in Python - Learn about deep learning in recommender systems - Understand the benefits and challenges of deep learning in recommender systems recommendation systems - Implement generic deep learning-based approaches for recommender systems - Learn about neural network models for recommendations - Explore theoretical aspects of neural collaborative filtering and variational autoencoders for collaborative filtering - Gain practical experience in implementing deep learning-based recommender systems - Implement the two-tower model for developing recommender systems - Use TensorFlow recommenders for developing recommender systems - And much more...