Unsupervised Learning: Clustering and Dimensionality Reduction
K-means, DBSCAN, PCA, t-SNE and anomaly detection.
Data Scientist
K-means, DBSCAN, PCA, t-SNE and anomaly detection.
ARIMA, Prophet and gradient boosting for demand and sales forecasts.
The Andrew Ng-style core, hands-on in Python.
Collaborative filtering, embeddings and ranking for products and content.
tidyverse, ggplot2 and R Markdown reports from zero.
NumPy, pandas, Matplotlib and your first end-to-end analysis.
Distributions, Bayes and expected value — with Python simulations.
Teach machines to work with human language.
The linear algebra, probability and calculus you actually need.
Intuition-first ML for managers, analysts and curious beginners.
Read a competition, build a baseline, climb the leaderboard.
Clinical, claims and operational data — analysed responsibly.
The work that actually wins: features, validation and hyperparameters.
SHAP, LIME, feature importance and explaining decisions to stakeholders.
Notebooks, environments, version control and projects that others can rerun.
Fix missing values, duplicates and dirty text — and keep data trustworthy.
Train, evaluate and ship real models with scikit-learn.
The core ideas behind models that learn from data.