Machine Learning Course Index – Dataplexa
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Machine Learning Course Index

A complete end-to-end Machine Learning roadmap — covering ML foundations, math, algorithms, feature engineering, model training, tuning, real-world ML pipelines, deep learning fundamentals, and deployment.

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I. Beginner Level (15 Lessons)

1. Introduction to Machine Learning 2. Types of Machine Learning 3. Machine Learning Workflow 4. Data Preprocessing 5. Feature Scaling 6. Data Cleaning 7. Data Visualization for ML 8. Statistics for ML 9. Linear Algebra (Basics) 10. Probability for ML 11. Overfitting & Underfitting 12. Train/Test Split 13. Cross-Validation 14. Bias–Variance Tradeoff 15. ML Evaluation Metrics

II. Intermediate Level (15 Lessons)

16. Linear Regression 17. Logistic Regression 18. Decision Trees 19. Random Forest 20. Gradient Boosting 21. XGBoost 22. Support Vector Machines 23. KNN Algorithm 24. Naive Bayes 25. Clustering (K-Means) 26. Hierarchical Clustering 27. Dimensionality Reduction 28. PCA (Principal Component Analysis) 29. Feature Selection 30. Feature Engineering

III. Advanced Level (15 Lessons)

31. Hyperparameter Tuning 32. Grid Search 33. Random Search 34. ML Pipelines 35. Model Deployment 36. Model Monitoring 37. Reinforcement Learning 38. Neural Networks (Basics) 39. Intro to Deep Learning 40. Regularization Techniques 41. Loss Functions 42. Optimizers 43. Transfer Learning 44. ML Case Studies 45. Mini ML Project