Machine Learning Tutorial: A Complete Guide
Your beginner-friendly journey into Machine Learning — the field that teaches computers to learn from data. Each chapter includes explanations, code examples, diagrams, and a hands-on exercise.
Table of Contents
- 1. Getting Started Introduction, Basics, Ecosystem, Applications, Life Cycle, Challenges
- 2. ML Foundations Math Foundations, AI Basics, Neural Networks, Deep Learning, Datasets
- 3. Python Ecosystem NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Jupyter
- 4. Data Preparation Loading Data, Categorical Data, Cleaning, Scaling, Train/Test Split
- 5. Models & Learning Types Supervised, Unsupervised, Semi-supervised, Reinforcement, Comparison
- 6. Supervised Learning Regression, Classification, SVM, Decision Trees, Random Forest
- 7. Unsupervised Learning Clustering, K-Means, DBSCAN, Hierarchical, Dimensionality Reduction
- 8. Reinforcement Learning Q-Learning, SARSA, Actor-Critic, Monte Carlo, DQN
- 9. Visualization Histograms, Density Plots, Box Plots, Correlation Matrix, Scatter Matrix
- 10. Statistics Mean/Median/Mode, Std Dev, Distributions, Bias/Variance, Hypothesis Testing
- 11. Regression Linear, Simple Linear, Multiple Linear, Polynomial Regression
- 12. Classification Logistic Regression, KNN, Naive Bayes, Decision Trees, Confusion Matrix
- 13. Clustering K-Means, K-Medoids, Mean-Shift, DBSCAN, OPTICS, BIRCH, Affinity Propagation
- 14. Dimensionality Reduction Feature Selection, PCA, Backward Elimination, Variance/Corelation Filters
- 15. Deep & Quantum ML Deep Q-Networks, Policy Gradient, Quantum ML Concepts
- 16. Quantum Machine Learning Quantum ML Concepts, QML with Python, Hybrid Models
- 17. Miscellaneous Performance Metrics, Boosting, Bagging, Cross Validation, ROC, Grid Search
- 18. MLOps Automated Workflows, Deployment, Monitoring, Data Leakage, Monetization
- 19. Practice & Resources Quick Guide, Cheatsheet, Interview Questions, Study Plan, Projects
- 20. References ML Reference, Libraries Reference, API Reference