Learn how to structure a business problem as an ML problem, and then go on to build, select and evaluate the right model. This workshop is designed to help learn how to apply machine learning to business problems. Real-life case studies are used to teach the various algorithms and techniques. The focus will be on applications, rather than on exposition of the various algorithms. The workshop is divided into following major 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)

Prerequisites Applied ML

• Knowledge of Python Programming
• Have experience working in pandas and jupyter notebooks

Duration of Workshop: 8 hours

Venue: Great Learning, Plot No. 758 – 759, 1st Floor, 19th Main Rd, Sector 2, HSR Layout, (Near Sri Sai Mandir), Garden Layout, Sector 2, HSR Layout, Bengaluru, Karnataka 560102 (Maps)

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