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 

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 resultsThey are why Google is the most successful technology company today.YouTube: Video dashboardI’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 scratchOther 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 arithmeticFor advanced sections, know calculus, linear algebra, and probability for a deeper understandingBe proficient in Python and the Numpy stack (see my free course)For the deep learning section, know the basics of using KerasFor the RBM section, know TensorflowWHAT 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) 

 

Overview 

 

Section 1: Welcome 

 

Lecture 1 Introduction 

 

Lecture 2 Outline of the course 

 

Lecture 3 Where to get the code 

 

Lecture 4 How to Succeed in this Course 

 

Section 2: Simple Recommendation Systems 

 

Lecture 5 Section Introduction and Outline 

 

Lecture 6 Perspective for this Section 

 

Lecture 7 Basic Intuitions 

 

Lecture 8 Associations 

 

Lecture 9 Hacker News - Will you be penalized for talking about the NSA? 

 

Lecture 10 Reddit - Should censorship based on politics be allowed? 

 

Lecture 11 Problems with Average Rating & Explore vs. Exploit (part 1) 

 

Lecture 12 Problems with Average Rating & Explore vs. Exploit (part 2) 

 

Lecture 13 Bayesian Ranking (Beginner Version) 

 

Lecture 14 Demographics and Supervised Learning 

 

Lecture 15 PageRank (part 1) 

 

Lecture 16 PageRank (part 2) 

 

Lecture 17 Evaluating a Ranking 

 

Lecture 18 Section Conclusion 

 

Lecture 19 Suggestion Box 

 

Section 3: Collaborative Filtering 

 

Lecture 20 Collaborative Filtering Section Introduction 

 

Lecture 21 User-User Collaborative Filtering 

 

Lecture 22 Collaborative Filtering Exercise Prep 

 

Lecture 23 Data Preprocessing 

 

Lecture 24 User-User Collaborative Filtering in Code 

 

Lecture 25 Item-Item Collaborative Filtering 

 

Lecture 26 Item-Item Collaborative Filtering in Code 

 

Lecture 27 Collaborative Filtering Section Conclusion 

 

Section 4: Beginner Q&A 

 

Lecture 28 How do I Choose Which Model to Use? 

 

Lecture 29 How do I Solve the Cold-Start Problem? 

 

Lecture 30 What if I Don't Like Math or Programming? 

 

Section 5: Matrix Factorization and Deep Learning 

 

Lecture 31 Matrix Factorization Section Introduction 

 

Lecture 32 Matrix Factorization - First Steps 

 

Lecture 33 Matrix Factorization - Training 

 

Lecture 34 Matrix Factorization - Expanding Our Model 

 

Lecture 35 Matrix Factorization - Regularization 

 

Lecture 36 Matrix Factorization - Exercise Prompt 

 

Lecture 37 Matrix Factorization in Code 

 

Lecture 38 Matrix Factorization in Code - Vectorized 

 

Lecture 39 SVD (Singular Value Decomposition) 

 

Lecture 40 Probabilistic Matrix Factorization 

 

Lecture 41 Bayesian Matrix Factorization 

 

Lecture 42 Matrix Factorization in Keras (Discussion) 

 

Lecture 43 Matrix Factorization in Keras (Code) 

 

Lecture 44 Deep Neural Network (Discussion) 

 

Lecture 45 Deep Neural Network (Code) 

 

Lecture 46 Residual Learning (Discussion) 

 

Lecture 47 Residual Learning (Code) 

 

Lecture 48 Autoencoders (AutoRec) Discussion 

 

Lecture 49 Autoencoders (AutoRec) Code 

 

Section 6: Restricted Boltzmann Machines (RBMs) for Collaborative Filtering 

 

Lecture 50 RBMs for Collaborative Filtering Section Introduction 

 

Lecture 51 Intro to RBMs 

 

Lecture 52 Motivation Behind RBMs 

 

Lecture 53 Intractability 

 

Lecture 54 Neural Network Equations 

 

Lecture 55 Training an RBM (part 1) 

 

Lecture 56 Training an RBM (part 2) 

 

Lecture 57 Training an RBM (part 3) - Free Energy 

 

Lecture 58 Categorical RBM for Recommender System Ratings 

 

Lecture 59 RBM Code pt 1 

 

Lecture 60 RBM Code pt 2 

 

Lecture 61 RBM Code pt 3 

 

Lecture 62 Speeding up the RBM Code 

 

Section 7: Big Data Matrix Factorization with Spark Cluster on AWS / EC2 

 

Lecture 63 Big Data and Spark Section Introduction 

 

Lecture 64 Setting up Spark in your Local Environment 

 

Lecture 65 Matrix Factorization in Spark 

 

Lecture 66 Spark Submit 

 

Lecture 67 Setting up a Spark Cluster on AWS / EC2 

 

Lecture 68 Making Predictions in the Real World 

 

Section 8: Basics Review 

 

Lecture 69 (Review) Keras Discussion 

 

Lecture 70 (Review) Keras Neural Network in Code 

 

Lecture 71 (Review) Keras Functional API 

 

Lecture 72 (Review) How to easily convert Keras into Tensorflow 2.0 code 

 

Lecture 73 (Review) Confidence Intervals 

 

Lecture 74 (Review) Gaussian Conjugate Prior 

 

Section 9: Bayesian Ranking (Scary Version) 

 

Lecture 75 Bayesian Approach part 0 (Preparation) 

 

Lecture 76 Bayesian Approach part 1 (Optional) 

 

Lecture 77 Optional: Bayesian Approach part 2 (Sampling and Ranking) 

 

Lecture 78 Optional: Bayesian Approach part 3 (Gaussian) 

 

Lecture 79 Optional: Bayesian Approach part 4 (Code) 

 

Lecture 80 Why don't we just use a library? 

 

Section 10: Setting Up Your Environment (FAQ by Student Request) 

 

Lecture 81 Anaconda Environment Setup 

 

Lecture 82 How to How to install Numpy, Theano, Tensorflow, etc… 

 

Section 11: Extra Help With Python Coding for Beginners (FAQ by Student Request) 

 

Lecture 83 How to Code by Yourself (part 1) 

 

Lecture 84 How to Code by Yourself (part 2) 

 

Lecture 85 Proof that using Jupyter Notebook is the same as not using it 

 

Lecture 86 Python 2 vs Python 3 

 

Section 12: Effective Learning Strategies for Machine Learning (FAQ by Student Request) 

 

Lecture 87 How to Succeed in this Course (Long Version) 

 

Lecture 88 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? 

 

Lecture 89 Machine Learning and AI Prerequisite Roadmap (pt 1) 

 

Lecture 90 Machine Learning and AI Prerequisite Roadmap (pt 2) 

 

Section 13: Appendix / FAQ Finale 

 

Lecture 91 What is the Appendix? 

 

Lecture 92 BONUS 

 

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