Core Algorithms

Core algorithms form the bedrock upon which machine learning models are constructed. They empower computers to discern patterns, make predictions, and drive intelligent decision-making. In this chapter, you'll gain insights into the essential algorithms that underpin machine learning applications.

You'll commence by exploring linear regression, a fundamental technique for predicting continuous outcomes. We'll delve into 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 versatile algorithm that partitions data into subsets based on feature values. Understanding decision trees is crucial, 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.