• Introduction to Machine Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning
  • Regression Analysis: Correlation, Simple Regression Models, R-Square, Multiple Regression, Multicollinearity, Individual Variable Impact
  • Logistic Regression: Need for Logistic Regression, Logistic Regression Models, Model Validation, Multicollinearity, Confusion Matrix
  • Decision Trees: Segmentation, Entropy, Information Gain, Building and Validating Decision Trees, Pruning, Fine-tuning, Prediction
  • Sentiment Analysis: Understanding Sentiment Analysis, Hands-on Sentiment Analysis using Twitter Data