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Master Machine Learning from mathematical foundations to cutting-edge deep learning. This comprehensive path covers linear algebra, probability, classical ML algorithms, neural networks, and modern techniques with 61 chapters and 1389+ pages.
This comprehensive learning path takes you from mathematical foundations to cutting-edge ML techniques, preparing you for ML engineering and research roles. **What you'll learn:** • Mathematical foundations (linear algebra, calculus, probability) • Statistical learning theory • Regression and classification algorithms • Ensemble methods (Random Forests, XGBoost) • Model evaluation and hyperparameter tuning • Unsupervised learning (clustering, dimensionality reduction) • Bayesian methods and probabilistic models • Neural networks and deep learning • CNNs, RNNs, and Transformers • Generative models and reinforcement learning • ML engineering and deployment Each topic includes detailed explanations with mathematical rigor.