Machine Learning Fundamentals: Learning to Make Recommendations

Machine Learning Fundamentals: Learning to Make Recommendations
Machine Learning Fundamentals: Learning to Make Recommendations

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1 Hour | 136 MB
eLearning | Skill level: Intermediate


This project-based course shows programmers of all skill levels how to use machine learning to build programs that can make recommendations. In this course, Adam Gеitgеy walks you through a hands-on lab building a recommendation system that is able to suggest similar products to customers based on past products they have reviewed or purchased. The system can also identify which products are similar to each other.

Recommendation systems are a key part of almost every modern consumer website. The systems help drive customer interaction and sales by helping customers discover products and services they might not ever find themselves. The course uses the free, open source tools Python 3.5, pandas, and numpy. By the end of the course, you’ll be equipped to use machine learning yourself to solve recommendation problems. What you learn can then be directly applied to your own projects.

Topics include:

  • Building a machine learning system
  • Training a machine learning system
  • Refining the accuracy of the machine learning system
  • Evaluating the recommendations received
+ Table of Contents

1 Welcome
2 What you should know before watching this course
3 Using the exercise files
4 Set up environment
5 What is a recommendation system_
6 What can you do with recommendation systems_
7 Cool uses of recommendation systems
8 Content-based recommendations – Recommending based on product attributes
9 Collaborative filtering – Recommending based on similar users
10 Introduction to NumPy, SciPy, and pandas
11 Think in vectors – How to work with large data sets efficiently
12 Explore our product recommendation data set
13 Represent product reviews as a matrix
14 Recommend by predicting missing user ratings
15 A simple way to predict missing user ratings
16 Latent representations of users and products
17 Code the recommendation system
18 How matrix factorization works
19 Use latent representations to find similar products
20 Explore our system’s recommendations
21 Use regularization
22 Measure recommendation accuracy
23 Make recommendations for existing users
24 How to handle first-time users
25 Find similar products
26 Wrap up