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 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' or Yep! Analytics professionals, modelers, big data professionals who haven't 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 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' or Yep! Analytics professionals, modelers, big data professionals who haven't 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 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
Target Audiences
- 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
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 – What's 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
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