Our Python Recommendation Systems Course is here to teach you about core concepts you need to know in Python and AI Machine Learning!
In this course you will learn about collaborative filtering, neighbourhood models, matrix factorisation, content-based filtering and much more!
You will be able to get a firm grasp on this essential Machine Learning component and put your skills to use once you have completed this intensive Recommendation Systems in Python course.
This course is 4.5 hours in length, and you will get 12 months access.
Just some of the subjects that you will focus on are:
-The Apriori Algorithm
-Installing Python using Anaconda and PIP
-Movielens and Pandas
And much more!
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Unlimited access for 12 months
Access anywhere, any time
Fast effective training, written and designed by industry experts
Track your progress with our Learning Management System
Save money, time and travel costs
Learn at your own pace and leisure
Easier to retain knowledge and revise topics than traditional methods
Exam preparation quizzes, tests and mock exams to ensure that you are 100% ready
£239.00 Add to cart
Course Length: 4.5 Hours
Chapter 01: Would You Recommend to a Friend?
Lesson 01: Introduction: You, This Course & Us!
Lesson 02: What do Amazon and Netflix have in common?
Lesson 03: Recommendation Engines: a look inside
Lesson 04: What are you made of? Content-Based Filtering
Lesson 05: With a little help from friends: Collaborative Filtering
Lesson 06: A Model for Collaborative Filtering
Lesson 07: Top Picks for You! Recommendations with Neighborhood Models
Lesson 08: Discover the Underlying Truth: Latent Factor Collaborative Filtering
Lesson 09: Latent Factor Collaborative Filtering continued
Lesson 10: Gray Sheep & Shillings: Challenges with Collaborative Filtering
Lesson 11: The Apriori Algorithm for Association Rules
Chapter 02: Recommendation Systems in Python
Lesson 01: Installing Python : Anaconda & PIP
Lesson 02: Back to Basics: Numpy in Python
Lesson 03: Back to Basics: Numpy & Scipy in Python
Lesson 04: Movielens & Pandas
Lesson 05: Code Along: What’s my favorite movie? – Data Analysis with Pandas
Lesson 06: Code Along: Movie Recommendation with Nearest Neighbor CF
Lesson 07: Code Along: Top Movie Picks (Nearest Neighbor CF)
Lesson 08: Code Along: Movie Recommendations with Matrix Factorization
Lesson 09: Code Along: Association Rules with the Apriori Algorithm
Minimum specifications for the computer are:
Microsoft Windows XP, or later
Modern and up to date Browser (Internet Explorer 8 or later, Firefox, Chrome, Safari)
OSX/iOS 6 or later
Modern and up to date Browser (Firefox, Chrome, Safari)
Internet bandwidth of 1Mb or faster
Flash player or a browser with HTML5 video capabilities (We recommend Google Chrome)