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. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.

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

Learning Objectives

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 DataFrame

    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

$2,195

Length: 3.0 days (24 hours)

Level:

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29
Nov
Wednesday
10:00 AM ET -
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