Machine Learning Basics

What is ML?

Supervised Learning

Unsupervised Learning

Model Evaluation

Linear Regression: Predicting Continuous Values

Logistic Regression: Binary Classification

Decision Trees: Rule-Based Models

Feature Engineering: Preparing Data for ML

Support Vector Machines (SVMs): Powerful Classification Boundaries

Introduction to Neural Networks: The Dawn of Deep Learning

Ensemble Methods: Random Forests and Boosting

Clustering: K-Means and Beyond

Dimensionality Reduction: PCA for Data Insight

Improving Models: Overfitting, Underfitting, and Regularization

Hyperparameter Tuning: Optimizing Model Performance

Handling Imbalanced Datasets: Strategies for Skewed Data

Model Interpretability and Explainable AI (XAI): Understanding Why Models Decide

Recommender Systems: Personalized Suggestions and Discovery

Time Series Forecasting: Predicting Future Trends

Introduction to Natural Language Processing (NLP): Working with Text Data