learn-co-students / dsc-pipelines-intro-staff

Introduction

Introduction

This lesson summarizes the topics we'll be covering in section 39 and why they'll be important to you as a data scientist.

Objectives

You will be able to:

• Understand and explain what is covered in this section
• Understand and explain why the section will help you to become a data scientist

Building a Machine Learning Pipeline

By now, you know that a data science process is a flow of activities, from inspecting the data to cleaning it, transforming it, running a model and discussing the result. Wouldn't it be nice if there was a streamlined process to create a nice machine learning workflows? Enter Machine Learning Pipelines in scikit-learn!

In this section, you'll learn how you can use a pipeline to integrate several steps of the machine learning workflow. Additionally, you'll compare several classification techniques with each other, and integrate grid search in your pipeline so you can tune several hyperparameters in each of the machine learning models.

Summary

This section will quickly introduce how to create pipelines in scikit-learn, but it will then be up to you to explore the magic world of pipelining and practice all your machine learning knowledge gained in this module!