Building Recommendation Systems with Python
Course Objectives
This skills-focused ccombines 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
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
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
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