Supervised Learning: Regression
Having established the basics of machine learning and its core concepts, we now turn our attention to a specific category: supervised learning. This chapter focuses on regression tasks, where the objective is to predict a continuous numerical output. Examples include predicting house prices based on features like size and location, or estimating temperature based on historical data.
You will learn:
- How to define and identify regression problems.
- The fundamental principles behind Linear Regression, a common algorithm used to model linear relationships between variables.
- The concept of cost functions, specifically how they are used to measure the error or inaccuracy of a regression model.
- An overview of gradient descent, an optimization algorithm used to minimize the cost function and find the best parameters for the model.
- How to interpret the results of a simple linear regression model.
We will illustrate these concepts with a practical example, guiding you through the steps of applying simple linear regression.
Sections
3.1 Understanding Regression Problems
3.2 Introduction to Linear Regression
3.3 How Linear Regression Learns
3.4 Cost Functions: Measuring Error
3.5 Gradient Descent: Finding the Best Fit
3.6 Simple Linear Regression Example
3.7 Practice: Implementing Simple Linear Regression
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