Description

Type

Course
Level

Intermediate
Methodology

Online

Duration

Flexible
Start date

Different dates available

Understand How Online Recommendations Work by Building a Movie App
In this ’Recommendation Systems in Python’ online course, you’ll learn about key concepts such as content-based filtering, collaborative filtering, neighborhood models, matrix factorization, and more! By the time you’ve finished the training, you’ll be able to build a movie recommendation system in Python by mastering both theory and practice. Supplemental Material included!

Facilities

Online

Start date

Different dates availableEnrolment now open

About this course

Course objectives

Recommendation Engines perform a variety of tasks, but the most important one is to find products that are most relevant to the user. Follow along with this intensive Recommendation Systems in Python training course to get a firm grasp on this essential Machine Learning component.

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Subjects

Systems
Database
Functions
Machine Learning
Interfacing
Language Processing
Exception Handling
Python Language
Generating Spreadsheets
Collaborative Filtering
Neighborhood Models

Teachers and trainers (1)

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Teacher

Course programme

Chapter I: Would You Recommend to a Friend?

Lesson I: Introduction: You, This Course & Us!
Lesson II: What do Amazon and Netflix have in common?
Lesson III: Recommendation Engines: a look inside
Lesson IV: What are you made of? Content-Based Filtering
Lesson V: With a little help from friends: Collaborative Filtering
Lesson VI: A Model for Collaborative Filtering
Lesson VII: Top Picks for You! Recommendations with Neighborhood Models
Lesson VIII: Discover the Underlying Truth: Latent Factor Collaborative Filtering
Lesson IX: Latent Factor Collaborative Filtering continued
Lesson X: Gray Sheep & Shillings: Challenges with Collaborative Filtering
Lesson XI: The Apriori Algorithm for Association Rules

Chapter II: Recommendation Systems in Python

Lesson I: Installing Python : Anaconda & PIP
Lesson II: Back to Basics: Numpy in Python
Lesson III: Back to Basics: Numpy & Scipy in Python
Lesson IV: Movielens & Pandas
Lesson V: Code Along: What’s my favorite movie? – Data Analysis with Pandas
Lesson VI: Code Along: Movie Recommendation with Nearest Neighbor CF
Lesson VII: Code Along: Top Movie Picks (Nearest Neighbor CF)
Lesson VIII: Code Along: Movie Recommendations with Matrix Factorization
Lesson IX: Code Along: Association Rules with the Apriori Algorithm

Additional information

Highlights:

Learn about Movielens – a famous dataset with movie ratings
Use Pandas to read and play around with the data
Learn how to use Scipy and Numpy
Introduction to Latent Factor Methods
Introduction to Memory-based Approaches
Design & implement a Recommendation System in Python

See related categories

Machine Learning - Recommendation Systems in Python

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Machine Learning - Recommendation Systems in Python

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