All protocols
AI Fundamentals
Start from zero and build a real mental model of how machines learn. The first module clears the fog around AI and work: what automation can take, what augmentation gives back, and where you fit. From there you go inside the machine, from learning paradigms down to neurons, layers, and the training loop that ties them together.
Every concept comes with something to play with. You will build a perceptron by hand, watch gradient descent roll downhill, and catch a model overfitting before its metrics admit it.
What you'll be able to do
- Tell supervised, unsupervised and reinforcement learning apart, and spot which one a product is built on
- Trace a neural network end to end: neurons, activations, forward pass, backpropagation
- Read a training run like a dashboard: loss curves, learning rate, and the signs of overfitting
- Judge a model with the right metric (accuracy, precision, recall) instead of the headline number
- Reason about AI and jobs with the automation vs augmentation frame instead of headlines
Why AI Matters (to You)
Work, fear, opportunity, why you need to understand AI now.
0/5 lessons completed · ~41 min
How Machines Learn
Supervised, unsupervised, reinforcement, how machines learn.
0/5 lessons completed · ~41 min
Neural Networks Intro
Neurons, layers, activation, how a neural network works.
0/6 lessons completed · ~48 min