AI & Data Science: The Big Picture Discover what AI really is, how Machine Learning and Deep Learning fit together, and where data science connects it all. See AI in your daily life and understand the 10-lesson journey ahead.

Python Foundations for AI Master the Python building blocks every AI engineer needs: variables, data types, loops, functions, and classes. Write your first AI-ready scripts with real examples drawn directly from machine learning workflows.

Data Exploration & Visualization Load, inspect, and understand real datasets using Pandas and Matplotlib. Learn to handle missing data, filter and group records, and create charts that reveal hidden patterns before any model is built.

Math & Probability for AI Build the intuitions behind AI algorithms without needing a PhD. Understand vectors, matrices, probability distributions, and key statistics, then see exactly where each concept appears in real machine learning code.

Data Cleaning & Preprocessing Turn messy real-world data into AI-ready fuel. Handle missing values, remove duplicates, encode categories, scale features, and split your dataset correctly, the foundational skills that determine every model's success.

Supervised Learning: Regression Teach AI to predict numbers. Build Linear Regression and Random Forest models to forecast house prices, evaluate results with R² and RMSE, and learn to diagnose overfitting versus underfitting in your models.

Supervised Learning: Classification Build AI that recognizes patterns and assigns categories. Train multiple classifiers, read confusion matrices, interpret precision and recall, and handle imbalanced datasets, applied to a real disease detection project.

Unsupervised Learning Discover hidden structure in data without any labels. Use k-Means clustering to segment customers, apply PCA to compress dimensions, and visualize complex high-dimensional data, no labeled examples required.

Deep Learning & Neural Networks Understand how neural networks learn through layers, weights, and gradient descent. Build a classifier with Keras, then use transfer learning with MobileNetV2 to create a powerful image recognition model in minutes.

Capstone: Build Your Own AI Apply everything end-to-end: define a real problem, load and clean data, compare multiple models, evaluate honestly, and save a deployable model. Present your work confidently, you are now an AI builder.