Machine Learning

Detailed syllabus for the Machine Learning course.

• Introduction to Machine Learning
  • Overview of machine learning and its applications
  • Types of machine learning: Supervised, unsupervised, reinforcement
• Data Preprocessing
  • Data cleaning, feature scaling, and encoding
  • Splitting data into training and test sets
• Supervised Learning Algorithms
  • Linear and logistic regression
  • Decision trees and random forests
  • Support Vector Machines (SVM)
• Unsupervised Learning Algorithms
  • Clustering techniques: K-Means, Hierarchical Clustering
  • Dimensionality reduction: PCA, LDA
• Model Evaluation and Tuning
  • Evaluation metrics: accuracy, precision, recall, F1-score
  • Cross-validation and hyperparameter tuning