Machine Learning Roadmap — a step-by-step learning path covering Math & Computer Science Foundations, Core Machine Learning Concepts, Neural Networks Fund…
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Core math and CS fundamentals required for machine learning.
Learning paradigms, classical ML algorithms, and evaluation techniques.
Understand neural network architecture, activation functions, and training.
Explore CNNs, RNNs, Transformers, and Graph Neural Networks.
Learn autoencoders, GANs, diffusion models, and LLMs.
Learn RL fundamentals, Q-learning, policy gradients, and PPO.
Operationalize ML: deployment, monitoring, pipelines, and scalability.
Explore domain-specific AI in vision, NLP, healthcare, finance, and robotics.
Develop ethical, fair, and compliant AI systems.
Drive enterprise AI transformation and strategy.