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Building Recommender Systems with Machine Learning and AI is the name of the video tutorial series on how to design and implement recommender engines. In fact, in this course you will learn how to design and implement such a system with the help of Machine Learning and Artificial Intelligence. Recommendation engines are programs that, by analyzing different algorithms, are able to show you what you like. The course ahead will teach you from elementary to advanced levels.

The technology mentioned earlier has been used in many world-renowned software today. Also, having at least some programming experience, especially Python, can help you learn better. It should be noted that an introduction to Python is provided at the beginning of this course. At the end of the course you can develop your own suggestion engines by learning the concepts and skills you have learned.

Courses taught in this course:

  • Learn how to build a recommendation engine from scratch
  • Understand how to filter content using item features
  • Learn modeling-based techniques including, matrix factorization and SVD
  • Learning how to apply Machine Learning and Artificial Intelligence
  • Real-world challenges and solutions that elevate your skills
  • Combine many recommendation algorithms together in a hybrid and group approach
  • And…

Course specification:

  • English language
  • Duration: 9 hours 15 minutes
  • Number of courses: 110
  • Level of education: introductory to advanced
  • Instructor: TSundog Education by Frank Kane, Frank Kane
  • File format: mp4

Course Outlines Building Recommender Systems with Machine Learning and AI

Course content
110 lectures 09:15:46

Getting Started
7 lectures 33:33

Introduction to Python [Optional]
4 lectures 16:59

Evaluating Recommender Systems
9 lectures 39:51

A Recommender Engine Framework
4 lectures 18:23

Content-Based Filtering
5 lectures 27:17

Neighborhood-Based Collaborative Filtering
13 lectures 53:00

Matrix Factorization Methods
6 lectures 27:14

Introduction to Deep Learning [Optional]
20 lectures 02:27:07

Deep Learning for Recommender Systems
14 lectures 01:17:20

Scaling it Up
9 lectures 50:23

Real-World Challenges of Recommender Systems
11 lectures 35:32

Case Studies
4 lectures 18:40

Hybrid Approaches
2 lectures 07:11

Wrapping Up
2 lectures 03:44

Prerequisites of Building Recommender Systems with Machine Learning and AI

Building Recommender Systems with Machine Learning and AI Requirements

  • A Windows, Mac, or Linux PC with at least 3GB of free disk space.
  • Some experience with a programming or scripting language (preferably Python)
  • Some computer science background, and an ability to understand new algorithms.


Building Recommender Systems with Machine Learning and AI

Sample Movie Building Recommender Systems with Machine Learning

Installation guide

View with your favorite Player after Extract.

English subtitle

Quality: 720p

download link

Download part 1 – 1 GB

Download part 2 – 299 MB

File password (s):


1.29 GB

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