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Business Business Analytics & Intelligence Recommendation Engine

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
Rating: 4.6 out of 54.6 (2,526 ratings)
13,775 students
Created by Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], French [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

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)

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

12 sections • 91 lectures • 12h 13m total length

  • Preview03:09
  • Preview04:45
  • Where to get the code
    05:05
  • Anyone Can Succeed in this Course
    12:42

  • Preview04:19
  • 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 0 (Preparation)
    12:08
  • Bayesian Approach part 1 (Optional)
    11:07
  • Optional: Bayesian Approach part 2 (Sampling and Ranking)
    05:57
  • Optional: Bayesian Approach part 3 (Gaussian)
    08:23
  • Optional: Bayesian Approach part 4 (Code)
    12:01
  • Why don't we just use a library?
    05:40
  • 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
  • Suggestion Box
    03:03

  • 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

  • How do I Choose Which Model to Use?
    04:02
  • How do I Solve the Cold-Start Problem?
    04:29
  • What if I Don't Like Math or Programming?
    05:47

  • 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

  • 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 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

  • (Review) Keras Discussion
    06:48
  • (Review) Keras Neural Network in Code
    06:37
  • (Review) Keras Functional API
    04:26
  • (Review) How to easily convert Keras into Tensorflow 2.0 code
    01:49
  • (Review) Confidence Intervals
    10:11
  • (Review) Gaussian Conjugate Prior
    05:41

  • Windows-Focused Environment Setup 2018
    20:20
  • How to How to install Numpy, Theano, Tensorflow, etc...
    17:30

  • 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
  • Python 2 vs Python 3
    04:38

Instructor

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,144 Reviews
  • 422,505 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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