Building Recommendation Systems with Python

Course Objectives

This skills-focused course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern applied datascience, AI and machine learning experience into every classroom and hands-on project.

Working in a hands-on lab environment led by our expert instructor, attendees will

• 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

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional AI, machine learning, data science, programming, Python/R and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web.

Attending students should have the following incoming skills:

• 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

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

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

Course Materials

Each student will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and links (as applicable). Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.

Lab Setup Made Simple.   All course labs and solutions, data sets, Tableau course software (limited version, for course use only), detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment for the duration of the course. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants.