Machine Learning full tutorial

Machine Learning – Complete tutorial

This page lists all Machine Learning lessons from beginner to advanced. Every topic will link to a detailed post.

Beginner Level

1. What is Machine Learning, Artificaial Intelligence and Deep Learning? 2. Types of ML: Supervised, Unsupervised, Reinforcement 3. Setting Up Python, NumPy, Pandas, Matplotlib 4. Data Collection & Cleaning 5. Feature Engineering Basics 6. Train/Test Split & Cross Validation 7. Linear Regression (Beginner-Friendly) 8. Logistic Regression Explained Simply

Intermediate Level

9. Decision Trees & Random Forests 10. Support Vector Machines (SVM) 11. KNN, K-Means & Clustering 12. Feature Scaling, Normalization, Standardization 13. Gradient Descent & Variants 14. Bias-Variance Tradeoff 15. Model Evaluation Metrics (Accuracy, F1, ROC-AUC) 16. Hyperparameter Tuning (Grid & Random Search)

Advanced Level

17. Introduction to Neural Networks 18. Deep Learning with TensorFlow/PyTorch 19. CNNs for Image Classification 20. RNN, LSTM, GRU for Sequences 21. Transformers & Attention Mechanism 22. Transfer Learning 23. ML Pipelines, MLOps, and Deployment 24. Large Language Models (LLMs) Basics

Real-World ML Case Studies

25. House Price Prediction 26. Fraud Detection System 27. Image Classification Project 28. Sentiment Analysis on Tweets

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