In this course, we going to build an end-to-end Python machine learning project. You’ll learn how to use Scikit-Learn to build and tune a supervised learning model. Scikit-learn was initially developed by David Cournapeau as a Google summer of code project in 2007 and since then has become the de facto library used for machine learning in Python. Python is one of the most popular languages for machine learning and in the course we’ll gently introduce you to SciKit-Learn, a library designed for working with machine learning projects. Scikit-Learn, also known as sklearn, is Python's premier general-purpose machine learning library. Scikit-Learn's versatility makes it the best starting place for most ML problems. Scikit-Learn is great for beginners it offers a high-level interface for many tasks. This allows you to better practice the entire machine learning workflow and understand the big picture. We will also gently introduce you to the vernacular of machine learning. For example, a target variable is simply that thing we are trying to predict. A feature is often no more than a column in at table. You’ll get hands on experience with the process of machine learning. The process involves importing data, cleaning the data, training and testing, pre-processing and feature engineering. We are going to define new terms but we will skip the math and theory for now.