Bargava Subramanian

Bargava Subramanian, Amit Kapoor

Audience level:
9 a.m.–1 p.m.

Introduction to Machine Learning


This workshop will introduce the attendees to the core concepts of machine learning. The scikit-learn package is introduced. Using real-life examples, the data modeling framework is introduced:

Data ingestion -> Feature creation -> Feature selection -> Model creation -> Model validation -> Model selection


Machine Learning is the branch of data science where algorithms are used to learn from historical data and is used to predict on future/unseen data. It impacts our daily lives in a lot of ways : The recommended links we get when we use the search engine, making portfolio decisions for the financial market, identifying different kinds of prospective customers etc.

This workshop will take a hands-on approach. The following topics will be covered


  • What is Machine Learning

  • Different kinds of Machine Learning

    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • Some types of ML problems

    • Classification
    • Regression
    • Dimensionality reduction
    • Clustering

Introduction to scikit-learn

Introductory Model - Linear Models & Model Evaluation

  • Linear Regression
  • Logistic Regression
  • Model evaluation
    • Accuracy metrics (ROC, AUC, Precision, Recall F-score)

Handling overfitting

  • What is overfitting?
  • Motivation to bias-variance
  • Cross-validation
  • Regularization (high-level concept - no math here)

Tree-based model

  • Decision Tree
  • Ensemble models
  • Bagging and Boosting
  • Random Forest
  • Gradient Boosting Machines

The repo for the workshop will be:

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