Scaling Data: Part 1

Intermediate
12m
1,051
4.7/5

This lesson is the first in a two-part series that covers how to build machine learning pipelines using scikit-learn, a library for the Python programming language. This is a hands-on lesson containing demonstrations that you can follow along with to build your own machine learning models.

Learning Objectives

  • Understand the different preprocessing methods in scikit-learn
  • Perform preprocessing in a machine learning pipeline
  • Understand the importance of preprocessing
  • Understand the pros and cons of transforming original data into a machine learning pipeline
  • Deal with categorical variables inside a pipeline
  • Manage the imputation of missing values

Intended Audience

This lesson is intended for anyone interested in machine learning with Python.

Prerequisites

To get the most out of this lesson, you should be familiar with Python, as well as with the basics of machine learning. It's recommended that you take our Introduction to Machine Learning Concepts lesson before taking this one.

Resources

The resources related to this lesson can be found in the following GitHub repo: https://github.com/cloudacademy/ca-machine-learning-with-scikit-learn

About the Author
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Andrea Giussani
Data Scientist
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Andrea is a Data Scientist at Cloud Academy. He is passionate about statistical modeling and machine learning algorithms, especially for solving business tasks.

He holds a PhD in Statistics, and he has published in several peer-reviewed academic journals. He is also the author of the book Applied Machine Learning with Python.

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