Name: Recommender Systems and Deep Learning in Python
Rating: 4.7 (6311 reviews)

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Created byLazy Programmer Inc., Lazy Programmer Team

Last updated 2/2026

English

What you'll learn

Understand and implement accurate recommendations for your users using simple and state-of-the-art algorithms
Big data matrix factorization on Spark with an AWS EC2 cluster
Matrix factorization / SVD in pure Numpy
Matrix factorization in Keras
Deep neural networks, residual networks, and autoencoder in Keras
Restricted Boltzmann Machine in Tensorflow

Course content

15 sections • 94 lectures • 12h 49m total length

Introduction3:09
Outline of the course4:45
Where to get the code5:05
How to Succeed in this Course3:04

Section Introduction and Outline4:19
Perspective for this Section3:41
Basic Intuitions5:14
Associations4:43
Hacker News - Will you be penalized for talking about the NSA?7:28
Reddit - Should censorship based on politics be allowed?8:54
Problems with Average Rating & Explore vs. Exploit (part 1)10:58
Explore how to rank items with ratings using confidence intervals, compare average rating versus lower bound via Wilson's interval, extend to five-star ratings, and apply upvote/downvote semantics.
Problems with Average Rating & Explore vs. Exploit (part 2)7:39
Bayesian Ranking (Beginner Version)22:34
Demographics and Supervised Learning7:22
PageRank (part 1)11:12
PageRank (part 2)11:55
Evaluating a Ranking4:39
Section Conclusion4:10
Suggestion Box3:10
The lecture presents a suggestion box to collect feedback and improve the course by inviting input on background, course difficulty, missing explanations, and topics like CNNs and transformers.

Collaborative Filtering Section Introduction11:38
Explore collaborative filtering in Python by modeling the user-item rating matrix, distinguishing sparse data, and predicting user-specific scores with regression and mean squared error evaluation.
User-User Collaborative Filtering13:51
Collaborative Filtering Exercise Prep10:21
Data Preprocessing15:26
Prepare the rating data by zero-based user IDs and indexing movie IDs. Shrink the data to active users and movies, then build user-to-movie, movie-to-user, and rating dictionaries.
User-User Collaborative Filtering in Code16:06
Explore user-user collaborative filtering in python by building a simple, data-driven predictor using averages, deviations, and pearson-based weights, then evaluate with mean squared error on train and test sets.
Item-Item Collaborative Filtering9:15
Learn item-item collaborative filtering and its relation to user-user filtering, using Pearson correlation to find similar items and predict ratings with neighborhood-based scoring for faster, accurate recommendations.
Item-Item Collaborative Filtering in Code7:07
Collaborative Filtering Section Conclusion5:34

Matrix Factorization Section Introduction4:08
Matrix Factorization - First Steps15:27
Explore matrix factorization by decomposing the user-item matrix into W and U to predict ratings via dot products, using sparse representations and latent features for dimensionality reduction with small k.
Matrix Factorization - Training8:56
Matrix Factorization - Expanding Our Model8:04
Matrix Factorization - Regularization6:18
Matrix Factorization - Exercise Prompt1:15
Practice matrix factorization for recommender systems in Python by loading data, initializing w, b, u, c, and mu; train until convergence, plot cost, and report train and test MSE.
Matrix Factorization in Code6:17
Matrix Factorization in Code - Vectorized10:14
SVD (Singular Value Decomposition)7:48
Probabilistic Matrix Factorization6:06
Bayesian Matrix Factorization5:34
Matrix Factorization in Keras (Discussion)7:32
Re-implement matrix factorization in Keras using user and movie embeddings, dot-product predictions, and optional biases, with gradient descent training and connections to word embeddings.
Matrix Factorization in Keras (Code)7:14
Deep Neural Network (Discussion)2:51
Deep Neural Network (Code)2:43
Residual Learning (Discussion)2:03
Residual Learning (Code)1:59
Neural Collaborative Filtering (NCF)16:51
Autoencoders (AutoRec) Discussion10:14
Autoencoders (AutoRec) Code11:45
Implement auto rec in code by preprocessing ratings data, building a two-layer autoencoder, and training with a custom masked loss to predict missing ratings.

RBMs for Collaborative Filtering Section Introduction2:08
Intro to RBMs8:21
Motivation Behind RBMs6:51
Intractability3:11
Neural Network Equations7:43
Show how energy and probability in a restricted Boltzmann machine lead to neural network equations, deriving p(h|v) as the sigmoid of W·v plus bias via Bayes rule.
Training an RBM (part 1)11:34
Training an RBM (part 2)6:18
Training an RBM (part 3) - Free Energy7:20
Categorical RBM for Recommender System Ratings11:32
RBM Code pt 17:26
RBM Code pt 24:16
RBM Code pt 311:42
Speeding up the RBM Code7:53
Speed up RBM code by moving one-hot encoding, masking, and dot-product computations into TensorFlow, eliminating numpy preprocessing and improving runtime.

Big Data and Spark Section Introduction7:16
Setting up Spark in your Local Environment7:36
Learn to set up Spark locally across macOS, Ubuntu, and Windows workflows by installing Java, Scala, and Spark (or PySpark via pip) and testing with the Spark shell.
Matrix Factorization in Spark10:28
Spark Submit6:26
Learn to run a Python Spark job with spark submit, creating a spark context and coordinating a local master. Grasp the overhead and memory considerations when processing large gzipped datasets.
Setting up a Spark Cluster on AWS / EC212:38
Set up a spark cluster on aws using spark ec2, create key pair and credentials, launch master and slaves, run the job, then terminate to avoid costs.
Making Predictions in the Real World2:46
Learn to use Spark for real world predictions, serving content via an API while batch Spark jobs feed a database, with mean squared error evaluation and fallback when jobs fail.

Requirements

For earlier sections, just know some basic arithmetic
For advanced sections, know calculus, linear algebra, and probability for a deeper understanding
Be proficient in Python and the Numpy stack (see my free course)
For the deep learning section, know the basics of using Keras

Description

Believe it or not, almost all online businesses today make use of recommender systems in some way or another.

What do I mean by “recommender systems”, and why are they useful?

Let’s look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook.

Recommender systems form the very foundation of these technologies.

Google: Search results

They are why Google is the most successful technology company today.

YouTube: Video dashboard

I’m sure I’m not the only one who’s accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?

That’s right. Recommender systems!

Facebook: So powerful that world governments are worried that the newsfeed has too much influence on people! (Or maybe they are worried about losing their own power... hmm...)

Amazing!

This course is a big bag of tricks that make recommender systems work across multiple platforms.

We’ll look at popular news feed algorithms, like Reddit, Hacker News, and Google PageRank.

We’ll look at Bayesian recommendation techniques that are being used by a large number of media companies today.

But this course isn’t just about news feeds.

Companies like Amazon, Netflix, and Spotify have been using recommendations to suggest products, movies, and music to customers for many years now.

These algorithms have led to billions of dollars in added revenue.

So I assure you, what you’re about to learn in this course is very real, very applicable, and will have a huge impact on your business.

For those of you who like to dig deep into the theory to understand how things really work, you know this is my specialty and there will be no shortage of that in this course. We’ll be covering state of the art algorithms like matrix factorization and deep learning (making use of both supervised and unsupervised learning - Autoencoders and Restricted Boltzmann Machines), and you’ll learn a bag full of tricks to improve upon baseline results.

As a bonus, we will also look how to perform matrix factorization using big data in Spark. We will create a cluster using Amazon EC2 instances with Amazon Web Services (AWS). Most other courses and tutorials look at the MovieLens 100k dataset - that is puny! Our examples make use of MovieLens 20 million.

Whether you sell products in your e-commerce store, or you simply write a blog - you can use these techniques to show the right recommendations to your users at the right time.

If you’re an employee at a company, you can use these techniques to impress your manager and get a raise!

I’ll see you in class!

NOTE:

This course is not "officially" part of my deep learning series. It contains a strong deep learning component, but there are many concepts in the course that are totally unrelated to deep learning.

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

For earlier sections, just know some basic arithmetic
For advanced sections, know calculus, linear algebra, and probability for a deeper understanding
Be proficient in Python and the Numpy stack (see my free course)
For the deep learning section, know the basics of using Keras
For the RBM section, know Tensorflow

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

Who this course is for:

Anyone who owns or operates an Internet business
Students in machine learning, deep learning, artificial intelligence, and data science
Professionals in machine learning, deep learning, artificial intelligence, and data science

What you'll learn

Explore related topics

Course content

Welcome4 lectures • 16min

Simple Recommendation Systems15 lectures • 1hr 58min

Collaborative Filtering8 lectures • 1hr 29min

Beginner Q&A3 lectures • 14min

Matrix Factorization and Deep Learning20 lectures • 2hr 23min

Restricted Boltzmann Machines (RBMs) for Collaborative Filtering13 lectures • 1hr 36min

Big Data Matrix Factorization with Spark Cluster on AWS / EC26 lectures • 47min

Basics Review6 lectures • 36min

Bayesian Ranking (Scary Version)6 lectures • 55min

Quizzes0

Requirements

Description

Who this course is for: