Building Recommendation Systems with Python (TTAML0002)

This course shows you how to do just that. You'll learn the different kinds of recommenders used in the industry and 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 quickly. In this course, you'll 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. Join us to learn how to build industry-standard recommender systems, leveraging Python syntax skills. This is an applied AI course, so machine learning theory is only used to highlight how to build recommenders in this course.

Retail Price: $2,395.00

Next Date: Request Date

Course Days: 3


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WHAT YOU'LL LEARN

Join an engaging hands-on learning environment, where you’ll learn:

  • Understand 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 real 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 filtering

WHO SHOULD ATTEND?

Developers, Analysts, and other professionals interested in learning the tools and techniques needed to build recommendation systems.

PREREQUISITES

Before attending this course, you should have:

  • Basic to Intermediate IT skills
  • Basic Python syntax skills are recommended (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
  • Python for Data Science Primer
  • Introduction to Linux

COURSE OUTLINE

Getting Started with Recommender Systems

  • Technical requirements
  • What is a recommender system?
  • Types of recommender systems

Manipulating Data with the Pandas Library

  • Technical requirements
  • Setting up the environment
  • The Pandas library
  • The Pandas DataFrame
  • The Pandas Series

Building an IMDB Top 250 Clone with Pandas

  • Technical requirements
  • The simple recommender
  • The knowledge-based recommender

Building Content-Based Recommenders

  • Technical requirements
  • Exporting the clean DataFrame
  • Document vectors
  • The cosine similarity score
  • Plot description-based recommender
  • Metadata-based recommender
  • Suggestions for improvements

Getting Started with Data Mining Techniques

  • Problem statement
  • Similarity measures
  • Clustering
  • Dimensionality reduction
  • Supervised learning
  • Evaluation metrics

Building Collaborative Filters

  • Technical requirements
  • The framework
  • User-based collaborative filtering
  • Item-based collaborative filtering
  • Model-based approaches

Hybrid Recommenders

  • Technical requirements
  • Introduction
  • Case study and final project – Building a hybrid model


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