Machine Learning Foundations
Core concepts and algorithms of machine learning.
Learning Objectives
- Understand supervised and unsupervised learning
- Master classic ML algorithms
- Learn model evaluation methods
Topics
1. Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees / Random Forest
- SVM
2. Unsupervised Learning
- K-Means Clustering
- PCA
3. Model Evaluation
- Cross-validation
- Metrics (Accuracy, Precision, Recall, F1)