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Machine Learning Recommendation Engine Python

5( 2 REVIEWS )

554 STUDENTS

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Machine Learning Recommendation Engine Python

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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

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

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

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)

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

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.

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.

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

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

Entry 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)

Who Should Attend

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

Method of Assessment

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.

certification

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.

Other Information

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

What careers can I get with this qualification?

Data Scientist
Big Data Specialist
Data Architect
Data Analyst

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

Students feedback

5

2 ratings

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Jack Turner April 09, 2019

Great Course

A great course to master both theory and practice to build a great movie recommendation system in Python. The modules are very comprehensive and full of tips and tricks. Happy with the overall learning experience.
Ozzy Cove November 29, 2018

Useful Course

I found the course very interesting and it has inspired me to gain additional qualifications in this field. The course was full of useful details and helpful, and encouraged me to read further in this subject area.