Python and Recommendation Systems

This is a high-level list of topics covered in this course. Please see the detailed Agenda below • Get to grips with the different kinds of recommender systems • Master data-wrangling techniques using the pandas library • Building an IMDB Top 250 Clone • Build a content-based engine to recommend movies based on movie metadata • Employ data-mining techniques used in building recommenders • Build industry-standard collaborative filters using powerful algorithms • Building Hybrid Recommenders that incorporate content based and collaborative fltering

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Hours :

Hands on Labs

Requirements

  • This course is geared for attendees with Python and Recommendation Systems skills who wish to build recommendation systems is a familiarity with Python, and by the time you're finished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. Pre-Requisites: Students should have • Basic to Inte
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    rmediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. • Good foundational mathematics or logic skills • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
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Description

Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques Working in a hands-on learning environment, led by our Recommendation Systems expert instructor, students will learn about and explore: • Build industry-standard recommender systems • Only familiarity with Python is required • No need to wade through complicated machine learning theory to use this course.

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About the Instructor

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About the Instructor

Ernesto Lee is an impassioned blockchain entrepreneur and technologist from the Miami/Fort Lauderdale Area. For over 25 years Ernesto has been asking hard questions and pursuing tough answers. Ernesto was an original founding member, co-owner and the CTO of Blockchain Training Alliance and former Chief Solutions Architect at TechBlue.com. Presently, Ernesto is the CEO at Ernesto.Net and Engineer at Kaiser Permanente.  Ernesto’s career illustrates a lifelong commitment to pushing the envelope on innovation and growing opportunities for all around him.

As a graduate of Old Dominion University (BS, Physics), Virginia Tech (MS, Software Engineering), and Harvard Extension School (Graduate Certificate, Business Communication), Ernesto has always had a passion for technology and teaching. It has been a cornerstone of Ernesto’s career.