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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.
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Certificate of completion.
|Section 1: Would You Recommend To A Friend?|
You, This Course, and Us!Preview
|Recommendations - good quality, personalized recommendations - are the holy grail for many online stores. What is the driving force behind this quest?|
|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.|
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|
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.
|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.|
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.
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.
|Gray Sheep, Synonymy, Data Sparsity, Shilling Attacks etc are a few challenges that people face with Collaborative Filtering.|
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|
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'
|Numpy arrays are pretty cool for performing mathematical computations on your data.|
We continue with a basic tutorial on Numpy and Scipy
Movielens is a famous dataset with movie ratings. Use Pandas to read and play around with the data.
|We continue playing with Movielens data - lets find the top n rated movies for a user.|
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
|We've predicted the user's rating for all movies. Let's pick the top recommendations for a user.|
|Matrix Factorization was first used for recommendations during the Netflix challenge. Let's implement this on the Movielens data and find some recommendations!|
|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.|
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 :-)