Machine Learning Basics
Master the core algorithms and concepts of machine learning. This comprehensive course covers essential ML foundations: supervised vs unsupervised learning, regression, classification, clustering, model evaluation, bias-variance tradeoff, regularization, and ensemble methods.
Level: Beginner · Category: Machine Learning · Estimated time: 9 hours
Prerequisites
- Python for AI
- Mathematics for Machine Learning
Lessons
- What is Machine Learning? — Overview of ML paradigms: supervised, unsupervised, and reinforcement learning.
- Linear Regression — The simplest ML algorithm — ordinary least squares, gradient descent, and polynomial regression.
- Logistic Regression & Classification — Binary classification, sigmoid function, decision boundaries, and multi-class strategies.
- Decision Trees & Random Forests — Tree-based models, information gain, bagging, and ensemble power.
- Support Vector Machines — Maximum margin classifiers, kernel trick, and SVMs in practice.
- Unsupervised Learning — K-means clustering, hierarchical clustering, DBSCAN, and PCA.
- Model Evaluation & Validation — Train/test split, cross-validation, confusion matrices, ROC/AUC, and metrics.
- Bias-Variance Tradeoff & Regularization — Overfitting, underfitting, L1/L2 regularization, and early stopping.
- Gradient Boosting & XGBoost — Boosting, XGBoost, LightGBM — the top algorithms for tabular data.
Topics covered
python, sklearn, regression, classification, clustering, ensemble, model-evaluation