Python for AI & Data Science

Master the foundation of Python's data science ecosystem. Learn how to create, index, slice, and perform vectorized operations on NumPy arrays for fast, efficient numerical computing.

Go beyond basic arrays with NumPy's most powerful features: broadcasting for shape-compatible operations, linear algebra for solving real problems, and the modern Generator API for reproducible random numbers.

Learn the two core data structures that power Pandas: Series for labeled 1D arrays and DataFrames for 2D labeled tables. Master indexing, selection, filtering, grouping, and I/O for real-world data manipulation.

Master the unglamorous but critical skill of data cleaning with Pandas. Learn to detect and handle missing values, remove duplicates, fix data types, and standardize messy real-world datasets.

Bridge the gap between raw data and insights with Pandas transformation operations. Master merge, concat, groupby, pivot tables, melt, and apply to reshape and analyze data like a professional.

Learn Matplotlib the right way with the object-oriented API. Understand the Figure-Axes hierarchy, master line plots, scatter plots, bar charts, and histograms, and build multi-panel dashboards.

Move beyond basic plotting with Seaborn for statistical visualizations and Plotly for interactive dashboards. Learn when to use each tool and how to create compelling data stories.

Build a rigorous statistical toolkit with scipy.stats. Learn descriptive statistics, probability distributions, hypothesis testing, correlation analysis, ANOVA, confidence intervals, and A/B testing.

Follow a complete, real-world EDA workflow from loading data to documenting findings. Learn the five-phase approach that professional data scientists use to understand any dataset before building models.

Build working intuition for linear and logistic regression in scikit-learn. Learn to fit, evaluate, and interpret both algorithms with hands-on code, proper metrics, and regularization techniques.

Go beyond logistic regression with three powerful classifiers. Learn how decision trees split data, why random forests reduce overfitting, and when SVMs find the optimal boundary -- all with working scikit-learn code.

Transform raw data into signal-rich features with scaling, encoding, imputation, and feature selection techniques. Learn the preprocessing skills that separate amateur ML from production-grade systems.

Learn to evaluate models honestly with k-fold cross-validation and find optimal hyperparameters using grid search, random search, and Bayesian optimization with Optuna.

Master the ensemble techniques that dominate Kaggle competitions and production ML systems. From Random Forests to XGBoost to stacking, learn why combining models beats any single learner.

Discover hidden structure in unlabeled data with K-Means, DBSCAN, hierarchical clustering, PCA, t-SNE, and UMAP. Build a complete customer segmentation workflow from scratch.

Build reproducible, leak-proof ML workflows with scikit-learn Pipelines and ColumnTransformers. Learn to chain preprocessing, handle mixed data types, write custom transformers, and deploy production-safe models.

Go beyond accuracy metrics to truly understand your deep learning models -- with confusion matrices, gradient-based interpretability, systematic debugging workflows, and tools for building trust in model predictions.