Master the modern data stack – from Python and statistics to LLM-powered analytics, RAG pipelines, and production ML.
6 months
Live cohort + self-paced
Beginner to job-ready
Build practical skills with Python, pandas, NumPy, and the data-wrangling techniques you actually need.
Next, learn statistics and probability through code instead of relying only on theory.
Then, master classical ML, including regression, classification, clustering, and proper model evaluation.
Finally, explore deep learning with PyTorch and focus on the mathematics that matters in real applications.
Working with LLMs: embeddings, RAG, evals, and agentic data workflows.
Deploying models and dashboards - not just notebooks.
Every skill is practiced through builds – not slides.
Start with Python, Git, Linux basics, statistics, probability, and SQL. From there, move into regression, classification, clustering, feature engineering, and essential evaluation metrics.
Build practical machine learning skills by applying algorithms to real datasets. Along the way, learn how to select models, evaluate results, and improve performance.
Next, work with neural networks using PyTorch, followed by CNNs, RNNs, Transformers, and fine-tuning small models.
Build a ChatGPT-style assistant grounded in a real dataset and learn how to evaluate its responses
Train, evaluate, and deploy a classification model designed to identify transaction fraud.
Then, create time-series forecasts and present the results through an interactive Power BI or web dashboard.
Build an autonomous agent that explores a database and generates SQL to answer data questions.
No. We start from absolute basics in Python and ramp up quickly.
Limited seats. Real mentorship. Real outcomes.
