Have you ever wondered how to apply machine learning to business problems? This workshop is specially designed to help learn the concepts, tools and techniques involved. You will go through real-life case studies and experience how this is done in the industry.

The focus of this workshop will be on the machine learning pipeline data cleaning, feature engineering, model building and evaluation. You will also learn how to structure a business problem as an ML problem, and then go on to build, select and evaluate the model.

The workshop is divided into following major modules:

Prerequisites Applied ML

• Knowledge of Python Programming

• Have experience working in pandas and jupyter notebooks

STRUCTURE OF THE WORKSHOP

This is an 8-hour workshop and includes the following modules:

• Module 0: Introduction
  
  
  • What is Machine Learning
  • Types of ML: Supervised, Unsupervised, Reinforcement
  • Types of ML problems: Regression, Classification
• Module 1: Linear Models
  
  
  • Linear Regression
  • Logistic Regression
• Module 2: Model Evaluation
  
  
  • Training and Validation Model
  • Evaluation Metrics – Accuracy, RMSE, ROC, AUC, Confusion Matrix, Precision, Recall, F1 Score
  • Overfitting and Bias-Variance trade-off
  • Regularization (L1/L2)
  • K-fold Cross Validation
• Module 3: Tree-based Models
  
  
  • Decision Trees
  • Bagging and Boosting
  • Random Forest
  • Gradient Boosting Machines
  • Feature Importance
• Module 4: Model Selection
  
  
  • Model Pipelines
  • Feature Engineering
  • Ensemble Models (Advanced)
  • Unbalanced Classes (Advanced)

INSTRUCTORS

AMIT KAPOOR