Understanding and implementing artificial intelligence, from Python to the Arduino UNO Q

First steps with Python on the UNO Q: variables, rules, observations and first learning data

Build a first Machine Learning model capable of learning its parameters from data

Understand how a model gradually adjusts its weight to learn from data

Apply gradient descent to the watering model and discover the limits of a simple score

Transform a score with the sigmoid, discover a first artificial neuron, and make it learn

Understand why a single neuron is not always enough and build our first small neural network

Understand how a model uses tools and acts within a controlled scope