Core Algorithms

Core algorithms form the foundation upon which machine learning models are constructed. They help computers recognize patterns, make predictions, and drive intelligent decision-making. In this chapter, you'll gain insights into the important algorithms that support machine learning applications.

You'll start by looking into linear regression, a basic technique for predicting continuous outcomes. We'll cover the mathematical foundations of this algorithm, focusing on concepts like the line of best fit, with equations such as $y = mx + b$ guiding your understanding.

Next, you'll encounter logistic regression, which extends these ideas to classification tasks. You'll learn how logistic regression estimates probabilities and assigns binary labels, providing a bridge into more complex classification methods.

You'll also explore decision trees, a flexible algorithm that partitions data into subsets based on feature values. Understanding decision trees is critical, as they form the basis for more advanced ensemble methods covered later in the course.

By the end of this chapter, you'll have a solid grasp of these core algorithms, enabling you to apply them effectively in solving real-world machine learning problems.