Building a Recommendation System with Python Machine Learning & AI

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Created by: Lillian Pierson

Produced in 2017

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Course Description

Discover how to use Python - and some essential machine learning concepts - to build programs that can make recommendations. In this hands-on course, Lillian Pierson, P.E. covers the different types of recommendation systems out there, and shows how to build each one. She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises. Once you're familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best.

Intermediate Level

Time: 1h 38m

Instructor Details

Lillian Pierson

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Lillian Pierson, P.E. is a leading expert in the field of big data and data science.

She equips working professionals and students with the data skills they need to stay competitive in today's data-driven economy.

Lillian has recently become a data science instructor for multiple courses on LinkedIn Learning. She's also the author of several highly-referenced technical books by the John Wiley & Sons, Inc. publishing company- "including Data Science for Dummies (2017, 2015)- "and has spent the last decade training and consulting for large technical organizations in the private sector, such as IBM, BMC, Dell, and Intel, as well as government organizations, from the US Navy down to the local government level.

As the Founder of Data-Mania LLC, Lillian offers online and face-to-face training courses, as well as workshops and other educational materials in the area of big data, data science, and data analytics.