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Overview

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.

Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.

This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).

Who is it for?

  • Analytics professionals, modellers, 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

Course description:

This course consists of the following modules:

  • Module 01: Would You Recommend to a Friend?
  • Module 02: Recommendation Systems in Python

Course Duration:

From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.

Method of Assessment:

At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed.

Certification:

Successful candidates will be awarded a certificate for Machine Learning – Recommendation Systems in Python.

Entry Requirement:

Learners must be age 16 or over and should have a basic understanding of the English Language, numeracy, literacy, and ICT.

Career Path:

This course will provide you with the knowledge and skills to gain high level job roles in the following industries:

  • Machine learning
  • Quantitative research
  • Risk analysis
  • Quantitative trading

Presenter Information:

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum

PLEASE NOTE: We do not provide any software with this course.

 

 

Still not convinced?

Download our course brochure & learn more about this course.

Course Curriculum

Free Introduction
Machine Learning – Recommendation Systems in Python FREE 00:00:00
1: Would You Recommend to a Friend?
1. Introduction: You, This Course & Us! 00:00:00
2. What do Amazon and Netflix have in common? 00:00:00
3. Recommendation Engines: a look inside 00:00:00
4. What are you made of? Content-Based Filtering 00:00:00
5. With a little help from friends: Collaborative Filtering 00:00:00
6. A Model for Collaborative Filtering 00:00:00
7. Top Picks for You! Recommendations with Neighborhood Models 00:00:00
8. Discover the Underlying Truth: Latent Factor Collaborative Filtering 00:00:00
9. Latent Factor Collaborative Filtering continued 00:00:00
10. Gray Sheep & Shillings: Challenges with Collaborative Filtering 00:00:00
11. The Apriori Algorithm for Association Rules 00:00:00
2: Recommendation Systems in Python
1. Installing Python : Anaconda & PIP 00:00:00
2. Back to Basics: Numpy in Python 00:00:00
3. Back to Basics: Numpy & Scipy in Python 00:00:00
4. Movielens & Pandas 00:00:00
5. Code Along: What’s my favorite movie? – Data Analysis with Pandas 00:00:00
6. Code Along: Movie Recommendation with Nearest Neighbor CF 00:00:00
7. Code Along: Top Movie Picks (Nearest Neighbor CF) 00:00:00
8. Code Along: Movie Recommendations with Matrix Factorization 00:00:00
9. Code Along: Association Rules with the Apriori Algorithm 00:00:00

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    Course Reviews

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      Office Skills, Secretarial & PA Training 2

      I regret doing this course- only a few units were actually useful! *A lot of it is just common sense *The units on Microsoft Word and Outlook were shown using the obsolete versions *Some of the videos were shot & edited very poorly *Some of the written tutorials were shoddy and seemed 'rushed' *A couple of videos were looped *I couldn't find the exercise files for at least one of the units (not at the end where they usually are) *The mock exams and final exam had questions on things that weren't even in the course! All I got from this course is an idea of what I need to work on and how NOT to do it.
      Sally Eastman
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      Learnt how to work in a practical sense with those who really need you after taking this course.
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