Menu
Menu
  • No products in the basket.

Machine Learning Recommendation Engine Python Course

5( 2 REVIEWS )
554 STUDENTS
ACCREDITED BY

Course Description:

During this excellent Machine Learning – Recommendation Systems in Python learners will focus on key concepts such as content-based filtering, collaborative filtering, neighbourhood models, matrix factorization, and more! This Recommendation Systems in Python course will teach you to build a movie recommendation system in Python by mastering both theory and practice. If you work in analytics, big data, or just want to learn more about machine learning, this course is for you.

Our learning material is available to students 24/7 anywhere in the world, so it’s extremely convenient. These intensive online courses are open to everyone, as long as you have an interest in the topic! We provide world-class learning led by IAP, so you can be assured that the material is high quality, accurate and up-to-date.

What skills will I gain?

  • 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

What are the requirements?

  • You must be 16 or over
  • You should have a basic understanding of English, Maths and ICT
  • You will need a computer or tablet with internet connection (or access to one)

Meet the Instructor:

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

Course outline:

  • Module 01: Introduction: You, This Course & Us!
  • Module 02: What Do Amazon And Netflix Have In Common?
  • Module 03: Recommendation Engines: A Look Inside
  • Module 04: What Are You Made Of? Content-Based Filtering
  • Module 05: With a Little Help From Friends: Collaborative Filtering
  • Module 06: A Model for Collaborative Filtering
  • Module 07: Top Picks for You! Recommendations with Neighborhood Models
  • Module 08: Discover the Underlying Truth: Latent Factor Collaborative Filtering
  • Module 09: Latent Factor Collaborative Filtering Continued
  • Module 10: Gray Sheep & Shillings: Challenges With Collaborative Filtering
  • Module 11: The Apriori Algorithm for Association Rules
  • Module 12: Installing Python: Anaconda & Pip
  • Module 13: Back To Basics: Numpy in Python
  • Module 14: Back To Basics: Numpy & Scipy in Python
  • Module 15: Movielens & Pandas
  • Module 16: Code Along: What’s My Favorite Movie? – Data Analysis with Pandas
  • Module 17: Code Along: Movie Recommendation With Nearest Neighbor CF
  • Module 18: Top Picks for You! Recommendations with Neighborhood Models
  • Module 19: Discover the Underlying Truth: Latent Factor Collaborative Filtering
  • Module 20: Latent Factor Collaborative Filtering Continued
  • Module 21: Gray Sheep & Shillings: Challenges With Collaborative Filtering
  • Module 22: The Apriori Algorithm for Association Rules

How will I be assessed?

  • You will have one assignment. Pass mark is 65%.
  • You will only need to pay £19 for assessment.
  • You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days.

What Certification am I going to receive?

Those who successfully pass this course will be awarded a Machine Learning – Recommendation Systems in Python certificate. Anyone eligible for certification will receive a free e-certificate, and printed certificate.

What careers can I get with this qualification?

Once you have completed this Machine Learning – Recommendation Systems in Python course you will have desirable skills. You could go on to further study of machine learning and Python, or could gain entry level employment in this area. These roles often command a high salary, for example, the average salary of a Data Scientist in the UK is £38,455 (payscale.com). When you complete this Machine Learning – Recommendation Systems in Python, you could fulfil any of the following roles:

  • Data Scientist
  • Big Data Specialist
  • Data Architect
  • Data Analyst

Key Features

Gain an accredited UK qualification

Access to excellent quality study materials

Learners will be eligible for TOTUM Discount Card

Personalized learning experience

One year’s access to the course

Support by phone, live chat, and email

Course Curriculum Total Units : 22
1: Introduction: You, This Course & Us!
1. Introduction. You, This Course & Us!
2: What Do Amazon And Netflix Have In Common?
2. What Do Amazon And Netflix Have In Common?
3: Recommendation Engines: A Look Inside
3. Recommendation Engines. A Look Inside
4: What Are You Made Of? Content-based Filtering
4. What Are You Made Of? Content-Based Filtering
5: With A Little Help From Friends: Collaborative Filtering
5. With a Little Help From Friends. Collaborative Filtering
6: A Model For Collaborative Filtering
6. A Model for Collaborative Filtering
7: Top Picks For You! Recommendations With Neighborhood Models
7. Top Picks for You! Recommendations with Neighborhood Models
8: Discover The Underlying Truth: Latent Factor Collaborative Filtering
8. Discover the Underlying Truth. Latent Factor Collaborative Filtering
9: Latent Factor Collaborative Filtering Continued
9. Latent Factor Collaborative Filtering Continued
10: Gray Sheep & Shillings: Challenges With Collaborative Filtering
10. Gray Sheep & Shillings. Challenges With Collaborative Filtering
11: The Apriori Algorithm For Association Rules
11. The Apriori Algorithm for Association Rules
12: Installing Python : Anaconda & Pip
12. Installing Python. Anaconda & Pip
13: Back To Basics: Numpy In Python
13. Back To Basics. Numpy in Python
14: Back To Basics: Numpy & Scipy In Python
14. Back To Basics. Numpy & Scipy in Python
15: Movielens & Pandas
15. Movielens & Pandas
16: Code Along: What’s My Favorite Movie? – Data Analysis With Pandas
16. Code Along. What’s My Favorite Movie? – Data Analysis with Pandas
17: Code Along: Movie Recommendation With Nearest Neighbor Cf
17. Code Along. Movie Recommendation With Nearest Neighbor Cf
18: Top Picks For You! Recommendations With Neighborhood Models
18. Top Picks for You! Recommendations with Neighborhood Models
19: Discover The Underlying Truth: Latent Factor Collaborative Filtering
19. Discover the Underlying Truth. Latent Factor Collaborative Filtering
20: Latent Factor Collaborative Filtering Continued
20. Latent Factor Collaborative Filtering Continued
21: Gray Sheep & Shillings: Challenges With Collaborative Filtering
21. Gray Sheep & Shillings. Challenges With Collaborative Filtering
22: The Apriori Algorithm For Association Rules
22. The Apriori Algorithm for Association Rules
95% Off now!!! Hurry - limited time offer.
Use code “MAY95” at checkout
x