What are recommender systems?

When was the last time you watched a movie at a theatre? How about the last time you watched a movie at Netflix, Prime Video or Hotstar? If you’re like most consumers, you’ve done the second thing much more often.


But how much power do platforms like Netflix, Amazon, or WeChat, have on consumers and your potential customers? If you have spent any time on these sites you will know they are full of ‘personal’ recommendations. 


These online recommendations are based on algorithms which generate a series of suggestions for items that users might be interested in, and are based on previous choices.


For example, clicking and reading about ‘management’ generates recommendations for other articles about ‘management’. Using these algorithms in theory keeps consumers on the site by giving them more of what they want.

Effectiveness of Personalised Recommender Systems

Let us look at some stats for the top tech giants who are leveraging these algorithms to the maximum:

  • Netflix: 67% rented movies are from Recommendations

  • Google News: 38% more click-through rates are due to recommendations

  • Amazon: 35% sales are from Recommendations

As a product manager optimizing for revenue, good personalised recommendations can go a long way in effectively getting that extra revenue with minimal effort. Now that we understand the effectiveness of these tools in the industry, let's look at the what will you learn in this course

What are the learning objectives in ‘Recommender Systems with Python’ Course?

  • Understand the importance of Recommender Systems in industry

  • Detailed Taxonomy of types of Recommender Systems

  • Collaborative Filtering Methods

  • Content Based Recommender Systems

  • Knowledge Based & Hybrid Recommender Systems

  • Market Basket Analysis & Association Rules

  • Evaluation of Recommender Systems

  • 4 real life projects

Projects for Recommender Systems with Python

Movie Recommender System
Article Recommender System
Online Book Recommender System
Market Basket Analysis for a Super Market


Download Projects

What do you need to start with Recommender Systems with Python course?

Here’s what you’ll need

  • A working laptop/desktop with 4 GB RAM

  • A working Internet connection

  • Basic Python Knowledge

Course curriculum

  • 1
    Introduction to Recommender Systems
    • What is a recommender System?
    • Industries where recommender systems are super relevant
    • Structure of Course
    • Instructor Introduction
  • 2
    Non Personalised Recommender Systems
    • Introduction to Non Personalised Recommender Systems
    • Why Non Personalised Recommender Systems?
    • Weak Personalisation
    • Case Study (Creating a Movie Recommender using summary stats)
  • 3
    Association Rule Mining
    • What is association Rule Mining?
    • Terminologies
    • Market Basket Analysis & Brute Force Method
    • Apriori Algorithm
    • Solve Market Basket Analysis problem using Apriori Algorithm in Python
  • 4
    Personalised Recommender Systems
    • Personalised Recommender Systems
    • Types of Personalised Recommender Systems
    • What is Collaborative Filtering?
    • Implicit vs Explicit Ratings
  • 5
    User Based Collaborative Filtering
    • Steps for User Based Nearest Neighbour Collaborative Filtering
    • Similarity Measures
    • Generating Predictions
    • Implementation on movielens data
  • 6
    Item Based Collaborative Filtering
    • Memory Based vs Model Based Approaches
    • Item Based Nearest Neighbour Collaborative Filtering
    • Data Sparcity Issues
    • Implementation for Item Based Collaborative Filtering
  • 7
    Matrix Factorization Based Collaborative Filtering
    • Motivation & Intuition
    • SVD Primer
    • User & Item Feature Matrices
    • Funk-SVD
    • Limitations of Collaborative Filtering Methods
  • 8
    Evaluation Metrics
    • Evaluation of Recsys and difficulties
    • How to select the right metric?
    • Predictive Accuracy Metrics
    • Classification Accuracy Metrics
    • Rank Aware Metrics
    • Metrics Beyond Accuracy
    • Implementation of metrics in Python
  • 9
    Content Based Filtering
    • Introduction to CB Based Filtering
    • Content Representation and Item similarities
    • Term Frequencies & TFIDF
    • User & Item Profiles for Content Based Filtering
    • Implementation of CB Based Filtering
    • Limitations of Content Based Filtering Methods
  • 10
    Knowledge Based & Hybrid Recommenders
    • Introduction to Knowledge Based Recommender Systems
    • Case Based vs Constraint Based Recommender Systems
  • 11
    Hybrid Recommender Systems
    • Hybrid Recommender Systems
    • Types of Hybrid Recommenders

Instructor

  • Ankit Choudhary

    Ankit Choudhary

    Ankit is an IIT Bombay Graduate with a Masters and Bachelors in Electrical Engineering. He is a corporate trainer and leads the hackathon category at Analytics Vidhya. He is responsible for liaison with various companies to transform their data into data science competitions. He has conducted corporate trainings for a BFSI client on Basic and Advanced Machine Learning. He has finished in top 5 of multiple data science competitions and also conducted a workshop on how to win data science competitions at DataHack Summit 2019. He has previously worked as a lead decision scientist for Indian National Congress deploying statistical models (Segmentation, K-Nearest Neighbours) to help party leadership/Team make data-driven decisions. His motivation lies in putting data at the heart of business for data-driven decision making.

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