- Certificate of completion - Free
Python: Recommendation Systems in Python
Learning 247
Summary
Overview
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!
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.
Certificates
Certificate of completion
Digital certificate - Included
Description
Course Info
Requirements:
- No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.
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
Target Audience:
- Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
- Engineers who want to understand or learn machine learning and apply it to problems they are solving
- Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
- Tech executives and investors who are interested in big data, machine learning or natural language processing
- MBA graduates or business professionals who are looking to move to a heavily quantitative role
PACKAGE INCLUDES:
Length of Subscription: 12 Months Online On-Demand Access
Running Time: 4 hrs 30 min
Platform: Windows & MAC OS
Level: Beginner to Advanced
Project Files: Included
Learn anytime, anywhere, at home or on the go.
Stream your training via the internet, or download to your computer and supported mobile device, including iPad, iPhone, iPod Touch and most Android devices.
Course Outline
Chapter 01: Would You Recommend to a Friend?
Lesson 01: Introduction: You, This Course & Us!
Lesson 02: What do Amazon and Netflix have in common?
Lesson 03: Recommendation Engines: a look inside
Lesson 04: What are you made of? Content-Based Filtering
Lesson 05: With a little help from friends: Collaborative Filtering
Lesson 06: A Model for Collaborative Filtering
Lesson 07: Top Picks for You! Recommendations with Neighborhood Models
Lesson 08: Discover the Underlying Truth: Latent Factor Collaborative Filtering
Lesson 09: Latent Factor Collaborative Filtering continued
Lesson 10: Gray Sheep & Shillings: Challenges with Collaborative Filtering
Lesson 11: The Apriori Algorithm for Association Rules
Chapter 02: Recommendation Systems in Python
Lesson 01: Installing Python : Anaconda & PIP
Lesson 02: Back to Basics: Numpy in Python
Lesson 03: Back to Basics: Numpy & Scipy in Python
Lesson 04: Movielens & Pandas
Lesson 05: Code Along: What’s my favorite movie? – Data Analysis with Pandas
Lesson 06: Code Along: Movie Recommendation with Nearest Neighbor CF
Lesson 07: Code Along: Top Movie Picks (Nearest Neighbor CF)
Lesson 08: Code Along: Movie Recommendations with Matrix Factorization
Lesson 09: Code Along: Association Rules with the Apriori Algorithm
Who is this course for?
Target Audience:
- Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
- Engineers who want to understand or learn machine learning and apply it to problems they are solving
- Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
- Tech executives and investors who are interested in big data, machine learning or natural language processing
- MBA graduates or business professionals who are looking to move to a heavily quantitative role
Requirements
No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.
Questions and answers
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Legal information
This course is advertised on reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.
FAQs
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