Hands On Training

Building Recommendation Engines in Python

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

We expect personalized experiences online. This could be Netflix suggesting a series or an online retailer suggesting possible products. How do these recommendations come about? This course will show you how to create your own recommendation engine. Through hands-on activities, you will learn about both collaborative and content-based filtering. Next, you will learn how to measure similarities like the Jaccard distance and cosine similarity. You'll also learn how to evaluate the quality of recommendations based upon test data using root means square error (RMSE). This course will teach you how to use Python to create these systems in any industry.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Data Manipulation with pandas

Supervised Learning with scikit-learn

What You Will Learn

In this course, you’ll learn everything you need to know to create your own recommendation engine

Learn to build recommendation engines in Python using machine learning techniques

Through hands-on exercises, you’ll get to grips with the two most common systems, collaborative filtering and content-based filtering

You’ll learn how to measure similarities like the Jaccard distance and cosine similarity, and how to evaluate the quality of recommendations on test data using the root mean square error (RMSE)

Course Content

Expand all sections

Module 1: Introduction to Recommendation Engines

Module 2: Content-Based Recommendations

Module 3: Collaborative Filtering

Module 4: Matrix Factorization and Validating Your Predictions

Course Instructors

Robert O'Callaghan

Director of Data Science, Ordergroove

Rob enables retailers and brands to make themselves indispensable to their customers lives by anticipating purchasing needs. Throughout his career, Rob has focused on the analysis, visualization, and...