Overview

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This course teaches you to use Python, AI, machine learning, and deep learning to build recommender systems, from simple engines to hybrid ensemble recommenders. You'll start with an introduction to recommender systems and Python, evaluate systems, and explore the recommender engine framework. You'll learn content-based recommendations, neighborhood-based collaborative filtering, and methods like matrix factorization and SVD. The course covers applying deep learning and AI to recommendations, scaling datasets with Apache Spark, solving real-world challenges, and studying systems like YouTube and Netflix. By the end, you'll build recommendation systems to help users discover new products and content. You'll test and evaluate algorithms with Python, use K-Nearest-Neighbors, address large-scale issues, make session-based recommendations with neural networks, and compute recommendations with Apache Spark. This course is for developers with basic Python knowledge.

Syllabus

Getting Started

In this module, we will lay the foundation for the course by setting up the development environment with Anaconda, familiarizing you with the course materials, and introducing you to creating simple movie recommendations.

Introduction to Python

In this module, we will cover the essentials of Python programming, including basic syntax, data structures, and functions. We will also delve into Boolean expressions and loops through hands-on challenges.

Evaluating a Recommender System

In this module, we will explore various methods for evaluating recommender systems, including accuracy metrics, hit rates, and diversity measures. We will also review practical examples and quizzes to reinforce learning.

A Recommender Engine Framework

In this module, we will focus on the architecture of a recommender engine framework, guiding you through code walkthroughs and activities to implement and test various recommendation algorithms.

Content-Based Filtering

In this module, we will dive into content-based filtering methods, exploring metrics like cosine similarity and KNN. We will also conduct hands-on activities to produce and evaluate movie recommendations.

Neighborhood-Based Collaborative Filtering

In this module, we will cover neighborhood-based collaborative filtering techniques, including user-based and item-based methods. Practical exercises and activities will help solidify your understanding of these approaches.

Matrix Factorization Methods

In this module, we will explore matrix factorization methods like PCA and SVD, demonstrating how to apply these techniques to movie rating datasets. We will also focus on improving these methods through hyperparameter tuning.

Introduction to Deep Learning (Optional)

In this module, we will provide an optional deep dive into deep learning, covering fundamental concepts, neural network architectures, and practical implementations using TensorFlow and Keras.

Deep Learning for Recommender Systems

In this module, we will focus on applying deep learning to recommender systems, exploring techniques like Restricted Boltzmann Machines (RBM) and auto-encoders. We will also cover practical evaluation and tuning methods.

Scaling It Up

In this module, we will explore methods to scale up recommendation systems, including using Apache Spark for large-scale data processing and Amazon's DSSTNE and SageMaker for deploying scalable machine learning models.

Real-World Challenges of Recommender Systems

In this module, we will tackle real-world challenges faced by recommender systems, such as the cold start problem, filtering bubbles, and fraud. We will also explore solutions to these issues through practical exercises.

Case Studies

In this module, we will study real-world case studies of YouTube and Netflix, focusing on their recommendation strategies and the use of deep learning and hybrid approaches to enhance recommendation quality.

Hybrid Approaches

In this module, we will explore hybrid recommendation approaches, combining multiple algorithms to improve recommendation accuracy and diversity. Practical exercises will guide you through implementing and evaluating hybrid systems.

Wrapping Up

In this module, we will wrap up the course by summarizing key points, providing resources for further study, and introducing advanced topics and emerging trends in recommender systems to keep you up-to-date.