EncartaLabs

Building Recommendation Systems with Python

( Duration: 3 Days )

In Building Recommendation Systems with Python training course you will 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.

By attending Building Recommendation Systems with Python workshop, delegates will learn to:

  • 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

  • Basic to Intermediate IT skills
  • Basic Python syntax skills are recommended
  • Good foundational mathematics or logic skills
  • Basic Linux skills
  • Developers, Analysts, and other professionals interested in learning the tools and techniques needed to build recommendation systems.

COURSE AGENDA

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

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 4,000 Modules on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting http://encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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