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Book Overview & Buying
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Table Of Contents
Building Recommender Systems with Machine Learning and AI
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Building Recommender Systems with Machine Learning and AI
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Overview of this book
This course will teach you how to use Python, artificial intelligence (AI), machine learning, and deep learning to build a recommender system. From creating a simple recommendation engine to building hybrid ensemble recommenders, you will learn key concepts effectively and in a real-world context.
The course starts with an introduction to the recommender system and Python. Learn how to evaluate recommender systems and explore the architecture of the recommender engine framework. Next, you will learn to understand how content-based recommendations work and get to grips with neighborhood-based collaborative filtering. Moving along, you will learn to grasp model-based methods used in recommendations, such as matrix factorization and Singular Value Decomposition (SVD).
Next, you will learn to apply deep learning, artificial intelligence (AI), and artificial neural networks to recommendations and learn how to scale massive datasets with Apache Spark machine learning. Later, you will encounter real-world challenges of recommender systems and learn how to solve them. Finally, you will study the recommendation system of YouTube and Netflix and find out what a hybrid recommender is.
By the end of this course, you will be able to build real-world recommendation systems that will help users discover new products and content online.
All the resource files are added to the GitHub repository at:
https://github.com/packtpublishing/building-recommender-systems-with-machine-learning-and-ai
Table of Contents (14 chapters)
Getting Started
Introduction to Python
Evaluating a Recommender System
A Recommender Engine Framework
Content-Based Filtering
Neighborhood-Based Collaborative Filtering
Matrix Factorization Methods
Introduction to Deep Learning (Optional)
Deep Learning for Recommender Systems
Scaling It Up
Real-World Challenges of Recommender Systems
Case Studies
Hybrid Approaches
Wrapping Up
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