Recommendation Systems are the backbone of some of the most awesome products we see around today. If you have done shopping on Amazon or Flipkart you may have come across things like:
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Topics we will be covering in the workshop:
- What are Recommendation Systems?
- Definition
- Real life examples
- Defining data for recommendation systems
- Fundamentals of Machine Learning and Linear Algebra
- Types of machine learning algorithms
- The row and column picture
- Dimensionality Reduction
- Collaborative Filtering Approaches
- User – item approaches
- Item – item approaches
- Dimensionality Reduction in Collaborative Filtering
- The Cold Start Problem
- Content Based Approaches
- Hybrid approaches
- Mixing Collaborative Filtering and Content based approaches
- Evaluation Metrics
- Theoretical: Accuracy, Precision, Recall, ROC
- Practical Evaluation Metrics – Business Metrics, A/B Testing, Google Analytics
- Association Rule Mining and Market Basket Analysis
- Building APIs and scaling them up
- Search as a Recommendation system
- Deployment of these systems
Case Studies we will be covering in the workshop:
- Building a job recommendation system for a jobs portal
- Video recommender system similar to YouTube
- Optimizing product placement in Retail to increase sales
- Food discovery similar to Uber Eats
Hands on projects we will be doing in the workshop:
- Building a movie rating recommendation system (similar to Netflix)
- Building a Song Recommendation System
- Building a Semantic Search system
Prerequisites
The workshop is having both theory and hands-on parts.- No Machine Learning knowledge is required
- Basics of Python Programming are necessary to follow the hands-on part.
- System Setup
- Pre-Reads
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