Machine Learning Essentials with Python (TTML5506-P)

Course Outline

Machine Learning (ML) Overview

• Machine Learning landscape
• Machine Learning applications
• Understanding ML algorithms and models (supervised and unsupervised)

Machine Learning Environment

• Introduction to Jupyter notebooks/R-Studio

Machine Learning Concepts

• Statistics Primer
• Covariance, Correlation, and Covariance Matrix
• Errors, Residuals
• Overfitting/Underfitting
• Cross validation and bootstrapping
• Confusion Matrix
• ROC curve and Area Under Curve (AUC)

Feature Engineering (FE)

• Preparing data for ML
• Extracting features and enhancing data
• Data cleanup
• Visualizing Data
• Exercise: data cleanup
• Exercise: visualizing data
• Linear regression
• Simple Linear Regression
• Multiple Linear Regression
• Running LR
• Evaluating LR model performance

Logistic Regression

• Understanding Logistic Regression
• Calculating Logistic Regression
• Evaluating model performance

Classification: SVM (Supervised Vector Machines)

• SVM concepts and theory
• SVM with kernel

Classification: Decision Trees and Random Forests

• Theory behind trees
• Classification and Regression Trees (CART)
• Random Forest concepts

Classification: Naive Bayes

• Theory behind Naive Bayes
• Running NB algorithm
• Evaluating NB model

Clustering (K-Means)

• Theory behind K-Means
• Running K-Means algorithm
• Estimating the performance

Principal Component Analysis (PCA)

• Understanding PCA concepts
• PCA applications
• Running a PCA algorithm
• Evaluating results

Recommendation (collaborative filtering)

• Recommender systems overview
• Collaborative Filtering concepts

Time Permitting: Capstone Project

• Hands-on guided workshop utilizing skills learned throughout the course