Byte-Sized-Chunks: Recommendation Systems

🎓 Course Title: Byte-Sized-Chunks: Recommendation Systems

Headline: Build a movie recommendation system in Python - Master Both Theory and Practice!

🔍 What You'll Learn:

• Understanding the Basics: This course is an abridged version of our comprehensive 20+ hour course, 'From 0 to 1: Machine Learning & Natural Language Processing'. It's designed for those who want a taste of recommendation systems without the full immersion. While some undergraduate-level mathematics knowledge will be beneficial, it's not mandatory. Similarly, a working knowledge of Python is helpful but not strictly required to follow along with the provided source code.

👩‍🏫 Instructor Credentials: Taught by a Stanford-educated, ex-Googler and an IIT (Indian Institute of Technology), IIM (Indian Institute of Management) - educated ex-Flipkart lead analyst. With decades of practical experience in quant trading, analytics, and e-commerce, our instructors bring real-world expertise to the course.

Course Breakdown:

• Introduction to Recommendation Engines: These powerful tools go beyond mere task performance; their primary role is to recommend products that best suit the user's preferences. We'll delve into various methods, starting with content-based filtering, collaborative filtering, neighborhood models like Memory Based Approaches, and latent factor methods like Matrix Factorization.

• Exploring Different Filtration Methods:

  • Content-Based Filtering: Learn how to recommend products based on their attributes and descriptions.
  • Collaborative Filtering: Understand the general concept of finding similar users and items and its dominance in modern recommendation systems.
  • Neighborhood Models: Discover how to use different similarity measures, such as Euclidean Distance, Pearson Correlation, and Cosine Similarity, to find user-user or item-item similarities.
  • Latent Factor Methods: Uncover the hidden factors in user behavior using Matrix Factorization, a technique used by big players like Netflix.

• Hands-On with Python:

  • Get hands-on experience with the MovieLens dataset, which contains movie ratings from users.
  • Use Pandas to manipulate and analyze the data effectively.
  • Learn how to utilize Scipy and Numpy for scientific computing and mathematical computations, respectively.

Why Take This Course?

• Practical Skills: Gain practical skills in building recommendation systems with real-world applications.
• Industry-Relevant Data: Work with a dataset that's familiar to many, the MovieLens dataset.
• Versatile Tools: Get comfortable with Python libraries like Pandas, Scipy, and Numpy.
• Real Insights: Learn from seasoned experts who have decades of experience in fields where recommendation systems are crucial.

Join Us on This Byte-Sized Adventure into the World of Recommendation Systems! 🌟

Whether you're looking to expand your knowledge in machine learning and data science or aim to enhance your career with skills in analytics and e-commerce, this course is your stepping stone. Sign up now and start your journey towards mastering recommendation systems with Python!