Course Structure#

Module 3: Supervised Learning - Regression and Classification (Weeks 5-6)#

  • Focus: Key concepts and algorithms in supervised learning.

  • Topics include regression, classification algorithms, decision trees, SVM, and ensemble methods.

Week 6: Supervised Learning - Classification#

  • Lesson 26: Introduction to Classification and Logistic Regression in Python

  • Introduction to classification algorithms in machine learning.

  • Math Focus: Understanding the logistic function and its application in logistic regression.

  • Lesson 27: K-Nearest Neighbors (K-NN) Algorithm in Python

  • Learning and implementing the K-NN algorithm for classification.

  • Math Focus: Distance metrics (Euclidean, Manhattan) used in K-NN.

  • Lesson 28: Support Vector Machines (SVM) for Linear and Nonlinear Data

  • Implementing SVM for both linear and nonlinearly separable data.

  • Math Focus: Concept of hyperplanes, margin maximization, and kernel tricks.

  • Lesson 29: Decision Trees and Rule-Based Models in Python

  • Building and interpreting decision tree models.

  • Math Focus: Understanding entropy and information gain calculations.

  • Lesson 30: Naive Bayes Classifier Implementation

  • Implementing the Naive Bayes classification algorithm.

  • Math Focus: Basics of probability and Bayes’ theorem in the context of classification.