A practical path from Python and math foundations to machine learning, deep learning, LLMs, and deployable AI projects.
Build the Python, statistics, and linear algebra base needed to understand models instead of only calling APIs.
Use notebooks, arrays, data frames, plotting, and clean code for repeatable experiments.
Learn vectors, matrices, probability, distributions, hypothesis testing, and evaluation metrics.
Train, validate, tune, and explain supervised and unsupervised models.
Build baseline models, choose metrics, prevent leakage, and interpret model behavior.
Create features, split data correctly, tune hyperparameters, and compare models fairly.
Analyze false positives, leakage, bias, and metric tradeoffs on a real dataset before improving the model.
Move from neural network basics to transformers, computer vision, NLP, and modern generative AI workflows.
Understand tensors, datasets, autograd, training loops, checkpoints, and GPU usage.
Use pretrained models, tokenizers, datasets, fine-tuning, evaluation, and responsible model sharing.
Package models, serve predictions, track experiments, monitor quality, and build interview-ready projects.
Serve a model through an API, add validation, containerize it, and document the inference contract.
Practice ML system design, model tradeoff explanations, error analysis, and concise experiment reports.
Build a dataset-to-deployment project with a README, metrics, limitations, and demo UI.