Byte-Sized-Chunks: Recommendation Systems

Build a movie recommendation system in Python - master both theory and practice
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  • Lectures 20
  • Length 4.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 3/2016 English

Course 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


What are the 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.

What am I going to get from this course?

  • Identify use-cases for recommendation systems
  • Design and Implement recommendation systems in Python
  • Understand the theory underlying this important technique in machine learning

What 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

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Would You Recommend To A Friend?
You, This Course, and Us!
Preview
01:27
16:43
Recommendations - good quality, personalized recommendations - are the holy grail for many online stores. What is the driving force behind this quest?
10:45
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.
13:35

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

10:26
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
17:51

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.

09:41
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.
20:13

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.

12:09

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.

08:12
Gray Sheep, Synonymy, Data Sparsity, Shilling Attacks etc are a few challenges that people face with Collaborative Filtering.
18:31

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

Section 2: Recommendation Systems in Python
09:00

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'

18:05
Numpy arrays are pretty cool for performing mathematical computations on your data.
14:19

We continue with a basic tutorial on Numpy and Scipy

16:45

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

06:18
We continue playing with Movielens data - lets find the top n rated movies for a user.
18:10

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

06:16
We've predicted the user's rating for all movies. Let's pick the top recommendations for a user.
17:55
Matrix Factorization was first used for recommendations during the Netflix challenge. Let's implement this on the Movielens data and find some recommendations!
09:50
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.

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Instructor Biography

Loony Corn, A 4-person team;ex-Google; Stanford, IIM Ahmedabad, IIT

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) 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

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

Navdeep: longtime Flipkart employee too, and IIT Guwahati alum

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 :-)

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