The shift from writing rules to learning from data. And why it changed everything in software and AI.
When to use labeled data vs unlabeled data. The core split that shapes every ML project decision.
Train a real image classifier with zero code using Google's Teachable Machine. And understand every step of the ML pipeline.
The most explainable ML algorithm. And how combining hundreds of them (Random Forest) makes them even more powerful.
Activation functions, backpropagation, gradient descent. Understand the engine that powers modern AI.
Detect and fix the two biggest ML failure modes. Overfitting and systematic bias. Before they break your model.
How ChatGPT, Claude, and Gemini actually work. Transformers, tokenization, attention, and RLHF explained clearly.
Build a real classifier that reads movie reviews and predicts positive or negative. Full ML pipeline from data to deployment.
Accuracy alone will lie to you. Learn the metrics that actually tell you if your model works in the real world.
How AI sees and understands images. Convolutional networks, object detection, and real-world vision applications.
How AI understands and generates text. From tokenization to embeddings to the models behind ChatGPT.
How production ML actually works. Deployment, monitoring, model drift, and why most ML projects fail.