Comprehensive ML roadmap from mathematics fundamentals to deep learning and deployment.
Linear algebra, calculus, probability, and statistics — the mathematical foundations of ML.
Master Python with NumPy, Pandas, Matplotlib, and Seaborn for data analysis.
Linear/logistic regression, decision trees, SVM, ensemble methods, and model evaluation.
Clustering, dimensionality reduction, anomaly detection, and association rules.
Neural networks, CNNs, RNNs, LSTMs, Transformers, and attention mechanisms.
Text processing, word embeddings, sequence models, BERT, GPT, and LLMs.
Model deployment, Docker, Kubernetes, CI/CD, monitoring, and ML pipelines.