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 will get started with building and learning about recommenders as quickly as possible.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

This skills-focused combines 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

1

  • Getting Started with Recommender Systems

  • Technical requirements
    What is a recommender system?
    Types of recommender systems
2

  • Manipulating Data with the Pandas Library

  • Technical requirements
    Setting up the environment
    The Pandas library
    The Pandas Data Frame
    The Pandas Series
3

  • Building an IMDB Top 250 Clone with Pandas

  • Technical requirements
    The simple recommender
    The knowledge-based recommender
4

  • 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
5

  • Getting Started with Data Mining Techniques

  • Problem statement
    Similarity measures
    Clustering
    Dimensionality reduction
    Supervised learning
    Evaluation metrics
6

  • Building Collaborative Filters

  • Technical requirements
    The framework
    User-based collaborative filtering
    Item-based collaborative filtering
    Model-based approaches
7

  • Hybrid Recommenders

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

Audience

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.

Language

English

Prerequisites

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

$2,395

Length: 3.0 days (24 hours)

Level:

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