Building a Recommendation System with Python Machine Learning & AI

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Lillian Pierson, P.E.

1:38:56

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01 - Introduction

01 - Welcome.mp4

01:19

02 - Using the exercise files.mp4

01:18

02 - 1. Simple Approaches to Recommendation Systems

01 - Introducing core concepts of recommendation systems.mp4

04:22

02 - Popularity-based recommenders.mp4

09:38

03 - Evaluating similarity based on correlation.mp4

26:38

03 - 2. Machine Learning Recommendation Systems

01 - Classification-based collaborative filtering.mp4

11:24

02 - Model-based collaborative filtering systems.mp4

25:14

03 - Content-based recommender systems.mp4

08:55

04 - Evaluating recommendation systems.mp4

09:48

04 - Conclusion

01 - Next steps.mp4

00:20

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.

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Python

Machine Learning

Artificial Intelligence

Lillian Pierson, P.E.

Instructor's Courses

I have 17 years of experience launching and developing technology products and delivering strategic consulting services. Many of the products I’ve managed have been e-learning products that educate learners on how to apply data science, data strategy, and business strategy to increase profits for their companies. To date these products have been consumed by 1.7 MM+ learners and have generated over $6 MM in revenue for my clients. I have launched over 40 products globally, delivered in 4 different languages. My products & go-to-market strategies have supported organizations as large as Walmart, Amazon, Microsoft, Dell & the US Navy. In fact, over the last 10 years I’ve supported 10% of Fortune 100 companies. Besides my extensive business background, I’m also an accomplished data scientist & engineer, having held licensure as a Professional Engineer since 2014. Over the past 10 years, I've especially enjoyed my work in: ▪ Go-To-Market Strategy & Product Marketing Management: Defining MMPs, product & service launch planning & execution, driving direct growth through "Acquisition, Activation, Retention, Revenue & Referral" funnels ▪ Product Management & Leadership: Managing product roadmaps, prioritizing product backlogs, managing products through their entire lifecycle, from conception to execution, hypothesis testing, UX design, website & product analytics, Python / R, SQL, ML / AI, experimentation, A/B Testing ▪ Business Leadership: Managing cross-functional teams, managers & stakeholders, representing the voice-of-customers, working with engineering teams ▪ Tech Startup Mentoring: Mentored 14 data startup founders to earn $2.8 MM+ in cum. revenue in the first year of businesses ▪ Data Science Instructor: Over 1.7 MM+ learners - using Python to demonstrate & explain machine learning algorithms & recommendation systems ▪ Earned & maintained accredited licensure as a Professional Engineer Of course, the above is not all I did - I also ran the business, oversaw customer happiness management, managed team members / managers within Data-Mania & worked directly with clients to ensure our collaborations exceeded their expectations. I am most passionate about product growth & marketing for tech startups. You can take a peek at my CV by going to my website here: https://www.data-mania.com/blog/lillian-pierson-cv/ If you'd like to discuss the possibility of me supporting your company & team, I'd love to chat with you inside my LinkedIn direct messages (it is set to "Open"). Just click the "Message" button on my profile and I will see you there!

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