Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent. Supervised machine learning is the underlying method behind a large part of this. Supervised learning involves using some algorithm to analyze and learn from past observations, enabling you to predict future events. This course introduces you to one of the prominent modelling families of supervised Machine Learning called Regression. This course will teach you to implement supervised classification machine learning models in Python using the Scikit learn (sklearn) library. You will become familiar with the most successful and widely used classification techniques, such as:

• Linear Regression

• Polynomial Regression

• RANSAC Regression

• Decision Tree Regression

• Random Forest Regression

• Support Vector Regression

• Neural Networks

You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. The complete course is built on several examples where you will learn to code with real datasets. By the end of this course, you will be able to build machine learning models to make predictions using your data. The complete Python programs and datasets included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Get ready to do more learning than your machine!