Lesson 2.1: Machine Learning Basics

Introduction to machine learning: Machine Learning (ML) is a subset of AI that focuses on developing algorithms that can learn patterns from data. Instead of being explicitly programmed, these algorithms improve their performance through experience.

Supervised, unsupervised, and reinforcement learning:

• Supervised Learning: In this approach, the algorithm learns from labeled data. For instance, it can learn to recognize cats from images that are labeled as "cat" or "not cat."
• Unsupervised Learning: Here, the algorithm identifies patterns in data without any labels. Clustering similar data points is an example.
• Reinforcement Learning: This is about training agents to make sequences of decisions. Think of teaching a robot to navigate a maze; it learns by receiving rewards for good moves and penalties for bad ones.

Lesson 2.2: Neural Networks

Explaining neural networks: A neural network is a model inspired by the human brain. It consists of interconnected layers of nodes (neurons) that process and transmit information. Each connection has a weight, which is adjusted during training.

Role of neurons and layers:

• Neurons: These are the fundamental processing units. They take inputs, apply weights, and produce outputs.
• Layers: Neurons are organized into layers - input, hidden, and output. Information flows from input to output through hidden layers.

Lesson 2.3: Training and Learning

Data collection and preprocessing: Training a machine learning model requires data. This data needs to be cleaned and organized before it's fed into the model. The quality of data greatly influences the model's performance.

Training process and optimization: During training, the model adjusts its weights to minimize errors. Optimization algorithms help find the best weights for accurate predictions.

Lesson 2.4: Making Predictions

Inference and making decisions: After training, the model can make predictions or decisions based on new, unseen data. This is called inference. For instance, a trained model can predict whether an email is spam or not.

Using trained models in real life: Trained models are deployed in various applications, like self-driving cars, language translation, and even medical diagnoses.