Byte-Sized-Chunks- Recommendation Systems

Byte-Sized-Chunks: Recommendation Systems, available at $29.99, has an average rating of 4.2, with 20 lectures, based on 140 reviews, and has 3230 subscribers.

You will learn about Identify use-cases for recommendation systems Design and Implement recommendation systems in Python Understand the theory underlying this important technique in machine learning This course is ideal for individuals who are Nope! Please dont enroll for this class if you have already enrolled for our 21-hour course From 0 to 1: Machine Learning and NLP in Python or Yep! Analytics professionals, modelers, big data professionals who havent had exposure to machine learning or Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving or Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning or Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing or Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role It is particularly useful for Nope! Please dont enroll for this class if you have already enrolled for our 21-hour course From 0 to 1: Machine Learning and NLP in Python or Yep! Analytics professionals, modelers, big data professionals who havent had exposure to machine learning or Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving or Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning or Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing or Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role.

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Summary

Title: Byte-Sized-Chunks: Recommendation Systems

Price: $29.99

Average Rating: 4.2

Number of Lectures: 20

Number of Published Lectures: 20

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Identify use-cases for recommendation systems

Design and Implement recommendation systems in Python

Understand the theory underlying this important technique in machine learning

Who Should Attend

Nope! Please dont 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 havent 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

Target Audiences

Nope! Please dont 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 havent 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

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

Course Curriculum

Chapter 1: Would You Recommend To A Friend?

Lecture 1: You, This Course, and Us!

Lecture 2: What do Amazon and Netflix have in common?

Lecture 3: Recommendation Engines – A look inside

Lecture 4: What are you made of? – Content-Based Filtering

Lecture 5: With a little help from friends – Collaborative Filtering

Lecture 6: A Neighbourhood Model for Collaborative Filtering

Lecture 7: Top Picks for You! – Recommendations with Neighbourhood Models

Lecture 8: Discover the Underlying Truth – Latent Factor Collaborative Filtering

Lecture 9: Latent Factor Collaborative Filtering contd.

Lecture 10: Gray Sheep and Shillings – Challenges with Collaborative Filtering

Lecture 11: The Apriori Algorithm for Association Rules

Chapter 2: Recommendation Systems in Python

Lecture 1: Installing Python – Anaconda and Pip

Lecture 2: Back to Basics : Numpy in Python

Lecture 3: Back to Basics : Numpy and Scipy in Python

Lecture 4: Movielens and Pandas

Lecture 5: Code Along – Whats my favorite movie? – Data Analysis with Pandas

Lecture 6: Code Along – Movie Recommendation with Nearest Neighbour CF

Lecture 7: Code Along – Top Movie Picks (Nearest Neighbour CF)

Lecture 8: Code Along – Movie Recommendations with Matrix Factorization

Lecture 9: Code Along – Association Rules with the Apriori Algorithm

Instructors

Loony Corn
An ex-Google, Stanford and Flipkart team

Rating Distribution

1 stars: 5 votes

2 stars: 10 votes

3 stars: 25 votes

4 stars: 50 votes

5 stars: 50 votes

Frequently Asked Questions

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