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.
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.
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.
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:
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 |
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.
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.