As we dive into the world of linear and logistic regression, you'll discover how these powerful tools can be used to analyze complex biological data, uncover patterns, and make predictions that drive scientific discovery. Whether you're interested in understanding gene expression, predicting disease outcomes, or exploring ecological data, this lesson will equip you with the essential skills to harness the potential of machine learning in the biological sciences.
For this lesson the most important prerequisite will be having some understanding of the important packages discussed in the previous lesson. It’s also recommended that you have some experience or understanding of calculus. Although not required, a background in calculus may prove useful to fully understand certain portions of the course, such as the math behind concepts like the gradient descent.
Linear Regression
Linear regression is often considered one of the simplest algorithms in machine learning, but its value goes far beyond its simplicity. This algorithm serves as an essential building block for understanding the fundamental concepts that are critical in more complex models. Through linear regression, we explore key ideas like the cost function, which quantifies the error between predicted and actual values, and gradient descent, a powerful optimization technique used to minimize that error. By mastering these concepts in the context of linear regression, you'll gain the foundational knowledge needed to tackle more advanced machine learning techniques with confidence.
As you have learned in previous math classes, the equation of a line can generally be expressed as y = mx + b, where is b is the y-intercept is m the slope. It’s also import to keep in mind that our input, x, is not a single number of a list or an array. In many ML lessons you’ll see this linear equations rewritten as:
