Byte-Sized-Chunks: Recommendation Systems
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Byte-Sized-Chunks: Recommendation Systems

Build a movie recommendation system in Python - master both theory and practice
3.7 (16 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
826 students enrolled
Created by Loony Corn
Last updated 3/2016
English
Current price: $10 Original price: $20 Discount: 50% off
1 day left at this price!
30-Day Money-Back Guarantee
Includes:
  • 4.5 hours on-demand video
  • 26 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Identify use-cases for recommendation systems
  • Design and Implement recommendation systems in Python
  • Understand the theory underlying this important technique in machine learning
View Curriculum
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.
Description

Note: This course is a subset of our 20+ hour course 'From 0 to 1: Machine Learning & Natural Language Processing' so please don't sign up for both:-)

Prerequisites: 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.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

  • Recommendation Engines perform a variety of tasks - but the most important one is to find products that are most relevant to the user.
  • Content based filtering finds products relevant to a user - based on the content of the product (attributes, description, words etc).
  • Collaborative Filtering is a general term for an idea that users can help each other find what products they like. Today this is by far the most popular approach to Recommendations
  • Neighborhood models - also known as Memory based approaches - rely on finding users similar to the active user. Similarity can be measured in many ways - Euclidean Distance, Pearson Correlation and Cosine similarity being a few popular ones.
  • Latent factor methods identify hidden factors that influence users from user history. Matrix Factorization is used to find these factors. This method was first used and then popularized for recommendations by the Netflix Prize winners. Many modern recommendation systems including Netflix, use some form of matrix factorization.
  • Recommendation Systems in Python!
  • Movielens is a famous dataset with movie ratings.
  • Use Pandas to read and play around with the data.
  • Also learn how to use Scipy and Numpy


Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2-3 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!


Who is the target audience?
  • Nope! Please don't enroll for this class if you have already enrolled for our 21-hour course 'From 0 to 1: Machine Learning and NLP in Python'
  • Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
  • Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
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Curriculum For This Course
20 Lectures
04:16:11
+
Would You Recommend To A Friend?
11 Lectures 02:19:33

Recommendations - good quality, personalized recommendations - are the holy grail for many online stores. What is the driving force behind this quest?
Preview 16:43

Recommendation Engines perform a variety of tasks - but the most important one is to find products that are most relevant to the user. Content based filtering, collaborative filtering and Association rules are common approaches to do so.
Recommendation Engines - A look inside
10:45

Content based filtering finds products relevant to a user - based on the content of the product (attributes, description, words etc).

What are you made of? - Content-Based Filtering
13:35

Collaborative Filtering is a general term for an idea that users can help each other find what products they like. Today this is by far the most popular approach to Recommendations
With a little help from friends - Collaborative Filtering
10:26

Neighbourhood models - also known as Memory based approaches - rely on finding users similar to the active user. Similarity can be measured in many ways - Euclidean Distance, Pearson Correlation and Cosine similarity being a few popular ones.

A Neighbourhood Model for Collaborative Filtering
17:51

We continue with Neighbourhood models and see how to predict the rating of a user for a new product. Use this to find the top picks for a user.
Top Picks for You! - Recommendations with Neighbourhood Models
09:41

Latent factor methods identify hidden factors that influence users from user history. Matrix Factorization is used to find these factors. This method was first used and then popularized for recommendations by the Netflix Prize winners. Many modern recommendation systems including Netflix, use some form of matrix factorization.

Discover the Underlying Truth - Latent Factor Collaborative Filtering
20:13

Matrix Factorization for Recommendations can be expressed as an optimization problem. Stochastic Gradient Descent or Alternating least squares can then be used to solve that problem.

Latent Factor Collaborative Filtering contd.
12:09

Gray Sheep, Synonymy, Data Sparsity, Shilling Attacks etc are a few challenges that people face with Collaborative Filtering.
Gray Sheep and Shillings - Challenges with Collaborative Filtering
08:12

Association rules help you find recommendations for products that might complement the user's choices. The seminal paper on association rules introduced an efficient technique for finding these rules - The Apriori Algorithm

The Apriori Algorithm for Association Rules
18:31
+
Recommendation Systems in Python
9 Lectures 01:56:38

Anaconda's iPython is a Python IDE. The best part about it is the ease with which one can install packages in iPython - 1 line is virtually always enough. Just say '!pip'

Installing Python - Anaconda and Pip
09:00

Numpy arrays are pretty cool for performing mathematical computations on your data.
Back to Basics : Numpy in Python
18:05

We continue with a basic tutorial on Numpy and Scipy

Back to Basics : Numpy and Scipy in Python
14:19

Movielens is a famous dataset with movie ratings. Use Pandas to read and play around with the data.

Movielens and Pandas
16:45

We continue playing with Movielens data - lets find the top n rated movies for a user.
Code Along - What's my favorite movie? - Data Analysis with Pandas
06:18

Let's find some recommendations now. We'll use neighbour based collaborative filtering to find the users most similar to a user and then predict their rating for a movie

Code Along - Movie Recommendation with Nearest Neighbour CF
18:10

We've predicted the user's rating for all movies. Let's pick the top recommendations for a user.
Code Along - Top Movie Picks (Nearest Neighbour CF)
06:16

Matrix Factorization was first used for recommendations during the Netflix challenge. Let's implement this on the Movielens data and find some recommendations!
Code Along - Movie Recommendations with Matrix Factorization
17:55

The Apriori algorithm was introduced in a seminal paper that described how to mine large datasets for association rules efficiently. Let's work through the algorithm in Python.
Code Along - Association Rules with the Apriori Algorithm
09:50
About the Instructor
Loony Corn
4.3 Average rating
4,276 Reviews
32,458 Students
75 Courses
An ex-Google, Stanford and Flipkart team

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years  working in tech, in the Bay Area, New York, Singapore and Bangalore.

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

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!

We hope you will try our offerings, and think you'll like them :-)