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Articles Home » Artificial Intelligence and Machine Learning » Become an AI and Machine Learning Specialist, Part I

Become an AI and Machine Learning Specialist, Part I

Machine Learning and AI Foundations: Recommendations with Adam Geitgey

58m 7s • COURSE
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 Geitgey 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


0. INTRODUCTION
001 Welcome
002 What you should know
003 Using the exercise files
004 Set up environment



1. The Basics of Making Recommendations
005 What is a recommendation system_
006 What can you do with recommendation systems_
007 Cool uses of recommendation systems



2. Ways of Making Recommendations
008 Content-based recommendations - Recommending based on product attributes
009 Collaborative filtering - Recommending based on similar users



3. Getting to Know Our Tools
010 Introduction to NumPy, SciPy, and pandas
011 Think in vectors - How to work with large data sets efficiently



4. Building the Framework for Our Recommendation System
012 Explore our product recommendation data set
013 Represent product reviews as a matrix
014 Recommend by predicting missing user ratings
015 A simple way to predict missing user ratings



5. Collaborative Filtering with Matrix Factorization
016 Latent representations of users and products
017 Code the recommendation system
018 How matrix factorization works
019 Use latent representations to find similar products



6. Testing Our System
020 Explore our system's recommendations
021 Use regularization
022 Measure recommendation accuracy



7. Using the Recommendation System in a Real World Program
023 Make recommendations for existing users
024 How to handle first-time users
025 Find similar products



CONCLUSION
026 Wrap up



· Posted by Superadmin on January 21 2019 · 0 Comments · 1263 Reads · Print
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