Clustering & Classification

What is Clustering?

K-Means Algorithm

Classification Basics

Decision Trees

Logistic Regression

Evaluating Classification Models (Metrics & Confusion Matrix)

Hierarchical Clustering & DBSCAN

Support Vector Machines (SVMs)

Convolutional Neural Networks (CNNs) for Image Classification

Transfer Learning and Fine-Tuning for Classification

Ensemble Methods (Random Forests & Boosting)

Model Selection & Hyperparameter Tuning

Dimensionality Reduction with PCA

Introduction to Neural Networks for Classification

Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) for Clustering

Handling Imbalanced Datasets in Classification

Model Interpretability and Explainable AI (XAI) for Classification

Anomaly Detection Techniques

Text Classification and Natural Language Processing (NLP) Fundamentals

Introduction to MLOps: Deployment & Monitoring of Models