Grasp syntax, control flow, functions, and essential libraries like NumPy.
Review linear algebra, calculus, probability, and statistics for data modeling.
Work with data using Pandas and visualize insights with Matplotlib or Seaborn.
Clean data, engineer features, and explore patterns before modeling.
Implement supervised and unsupervised techniques using scikit-learn.
Use cross-validation, metrics, and hyperparameter search to improve models.
Build neural networks with TensorFlow or PyTorch for complex tasks.
Serve models via APIs, monitor performance, and maintain data pipelines.
Master prompt engineering, fine-tuning, and RAG architectures for LLMs.
Build autonomous agents with memory, tools, and multi-step reasoning capabilities.