Andrea Giussani - Building Machine Learning Pipelines with scikit-learn - Part One

This course 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 course 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 course is intended for anyone interested in machine learning with Python.
Prerequisites
To get the most out of this course, 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 course before taking this one.
Resources
The resources related to this course can be found in the following GitHub repo:https://github.com/cloudacademy/ca-machine-learning-with-scikit-learn