Building Recommender Systems with Machine Learning and AI

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Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you'll explore the inner workings of recommender systems, gaining hands-on experience with Python and various machine learning techniques. Starting with the basics, you'll quickly move to more advanced methods like content-based filtering, collaborative filtering, and matrix factorization. By building real-world systems, you'll develop the skills needed to evaluate and improve recommender system performance. As you advance, you'll dive into deep learning for recommender systems, experimenting with technologies like Restricted Boltzmann Machines (RBM) and Autoencoders. You'll also explore TensorFlow Recommenders and other state-of-the-art approaches for building scalable recommendation engines. This course is designed to help you build, test, and deploy sophisticated recommender systems that can be applied in various industries. This course is ideal for those interested in artificial intelligence, machine learning, and data science, especially those who want to build personalized systems to enhance user experience. It will benefit anyone looking to design, evaluate, and optimize recommendation algorithms, making it an excellent resource for aspiring data scientists, machine learning engineers, and AI specialists.

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.