Track
Data & AI
Statistics, machine learning, and the skills to turn raw data into models that predict and explain.
Reading Data
Load, clean, summarize, and visualize real datasets in Python — learn the exploratory data analysis workflow that every data scientist and analyst uses on day one.
How Neural Networks Learn
Forward passes, backpropagation, and gradient descent explained from scratch — build a neural network in NumPy and see exactly what happens during training.
Statistics
Distributions, sampling, hypothesis testing, and confidence intervals — the inferential toolkit you need to run experiments and read research papers critically.
Regression & Prediction
Linear, logistic, and polynomial regression with real datasets — learn to fit models, interpret coefficients, and know when your model is overfitting.
Probability in Machine Learning
Maximum likelihood, Naive Bayes, and generative models — the probabilistic foundations that power everything from spam filters to modern LLMs.
Bayesian Thinking
Prior beliefs, likelihoods, and posterior updates — learn the Bayesian reasoning behind A/B testing, recommendation systems, and adaptive experimentation.