Machine Learning

Course Outline

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Machine Learning provides the foundation for building systems that can learn from data. This course takes you from basic human learning concepts to advanced algorithms like Deep Learning and Ensemble methods.

Syllabus Overview

Unit 1: Introduction

  • Human Learning vs Machine Learning
  • Types of Learning
  • Well-Posed Learning Problems
  • Applications and Issues in ML

Unit 2: Regression

  • Data Pre-processing
  • Dimensionality Reduction
  • Linear and Polynomial Regression

Unit 3: Classification

  • Logistic Regression
  • K-Nearest Neighbours
  • Decision Trees & SVM

Unit 4: Unsupervised

  • Clustering Algorithms
  • Association Rule Learning

Prerequisites

  • Basic understanding of Statistics and Probability
  • Familiarity with Linear Algebra
  • Fundamental programming knowledge
Introduction to Machine Learning