Machine Learning Basics

Machine learning (ML) is a subset of AI that allows systems to learn from data and improve over time. In this chapter, we will explore the basics of machine learning, starting with the different types of learning. Supervised learning is when the algorithm learns from labeled data, such as a set of images with corresponding labels (e.g., “cat” or “dog”). We’ll also look at unsupervised learning, where the algorithm is tasked with finding hidden patterns in data without explicit labels.

A key concept in machine learning is training and testing a model. In supervised learning, you split the data into two sets: one for training the model and another for testing its performance. We’ll discuss metrics such as accuracy and precision to assess how well the model has learned. You’ll learn how algorithms like linear regression and decision trees can be applied to simple problems, like predicting house prices or classifying emails as spam or not.

This chapter will also introduce you to model evaluation, a process that helps you test the performance of your model. We’ll cover techniques like cross-validation to ensure your model is generalizable and not overfitting the training data.