Recommender Systems and Deep Learning in Python
4.7 (405 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,673 students enrolled

Recommender Systems and Deep Learning in Python

The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques
Bestseller
4.7 (405 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,673 students enrolled
Last updated 12/2018
English
English [Auto-generated]
Current price: $11.99 Original price: $199.99 Discount: 94% off
30-Day Money-Back Guarantee
This course includes
  • 11.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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 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.



HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

  • 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




TIPS (for getting through the course):

  • Watch it at 2x.

  • Take handwritten notes. This will drastically increase your ability to retain the information.

  • Write down the equations. If you don't, I guarantee it will just look like gibberish.

  • Ask lots of questions on the discussion board. The more the better!

  • The best exercises will take you days or weeks to complete.

  • Write code yourself, don't just sit there and look at my code. This is not a philosophy course!




WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

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
Course content
Expand all 83 lectures 11:20:50
+ Simple Recommendation Systems
17 lectures 02:09:42
Perspective for this Section
03:41
Basic Intuitions
05:14
Associations
04:43
Hacker News - Will you be penalized for talking about the NSA?
07:28
Reddit - Should censorship based on politics be allowed?
08:54
Problems with Average Rating & Explore vs. Exploit (part 1)
10:58
Problems with Average Rating & Explore vs. Exploit (part 2)
07:39
Bayesian Approach part 1 (Optional)
11:07
Bayesian Approach part 2 (Sampling and Ranking)
05:57
Bayesian Approach part 3 (Gaussian)
08:23
Bayesian Approach part 4 (Code)
12:01
Demographics and Supervised Learning
07:22
PageRank (part 1)
11:12
PageRank (part 2)
11:55
Evaluating a Ranking
04:39
Section Conclusion
04:10
+ Collaborative Filtering
8 lectures 01:29:18
Collaborative Filtering Section Introduction
11:38
User-User Collaborative Filtering
13:51
Collaborative Filtering Exercise Prep
10:21
Data Preprocessing
15:26
User-User Collaborative Filtering in Code
16:06
Item-Item Collaborative Filtering
09:15
Item-Item Collaborative Filtering in Code
07:07
Collaborative Filtering Section Conclusion
05:34
+ Matrix Factorization and Deep Learning
19 lectures 02:06:28
Matrix Factorization Section Introduction
04:08
Matrix Factorization - First Steps
15:27
Matrix Factorization - Training
08:56
Matrix Factorization - Expanding Our Model
08:04
Matrix Factorization - Regularization
06:18
Matrix Factorization - Exercise Prompt
01:15
Matrix Factorization in Code
06:17
Matrix Factorization in Code - Vectorized
10:14
SVD (Singular Value Decomposition)
07:48
Probabilistic Matrix Factorization
06:06
Bayesian Matrix Factorization
05:34
Matrix Factorization in Keras (Discussion)
07:32
Matrix Factorization in Keras (Code)
07:14
Deep Neural Network (Discussion)
02:51
Deep Neural Network (Code)
02:43
Residual Learning (Discussion)
02:03
Residual Learning (Code)
01:59
Autoencoders (AutoRec) Discussion
10:14
Autoencoders (AutoRec) Code
11:45
+ Restricted Boltzmann Machines (RBMs) for Collaborative Filtering
13 lectures 01:36:15
RBMs for Collaborative Filtering Section Introduction
02:08
Intro to RBMs
08:21
Motivation Behind RBMs
06:51
Intractability
03:11
Neural Network Equations
07:43
Training an RBM (part 1)
11:34
Training an RBM (part 2)
06:18
Training an RBM (part 3) - Free Energy
07:20
Categorical RBM for Recommender System Ratings
11:32
RBM Code pt 1
07:26
RBM Code pt 2
04:16
RBM Code pt 3
11:42
Speeding up the RBM Code
07:53
+ Big Data Matrix Factorization with Spark Cluster on AWS / EC2
6 lectures 47:10
Big Data and Spark Section Introduction
07:16
Setting up Spark in your Local Environment
07:36
Matrix Factorization in Spark
10:28
Spark Submit
06:26
Setting up a Spark Cluster on AWS / EC2
12:38
Making Predictions in the Real World
02:46
+ Basics Review
5 lectures 33:43
(Review) Keras Discussion
06:48
(Review) Keras Neural Network in Code
06:37
(Review) Keras Functional API
04:26
(Review) Confidence Intervals
10:11
(Review) Gaussian Conjugate Prior
05:41
+ Appendix
12 lectures 02:25:15
What is the Appendix?
02:48
Windows-Focused Environment Setup 2018
20:20
How to How to install Numpy, Theano, Tensorflow, etc...
17:30
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
How to Succeed in this Course (Long Version)
10:24
How to Code by Yourself (part 1)
15:54
How to Code by Yourself (part 2)
09:23
Proof that using Jupyter Notebook is the same as not using it
12:29
What order should I take your courses in? (part 1)
11:18
What order should I take your courses in? (part 2)
16:07
Python 2 vs Python 3
04:38
BONUS: Where to get discount coupons and FREE deep learning material
02:20