Feature Engineering for Machine Learning in Python Data Camp

Overview

Context

Every day you read about the amazing breakthroughs in how the newest applications of machine learning are changing the world. Often this reporting glosses over the fact that a huge amount of data munging and feature engineering must be done before any of these fancy models can be used. 

In this Feature Engineering for Machine Learning in Python course offered by Data Camp, you will learn how to do just that. You will work with Stack Overflow Developers survey, and historic US presidential inauguration addresses, to understand how best to preprocess and engineer features from categorical, continuous, and unstructured data. 

This course will give you hands-on experience on how to prepare any data for your own machine learning models.

What you will do during this course:

• You will explore what feature engineering is and how to get started with applying it to real-world data. You will load, explore and visualize a survey response dataset, and in doing so you will learn about its underlying data types and why they have an influence on how you should engineer your features. 
• You will be introduced to the reality of messy and incomplete data. You will learn how to find where your data has missing values and explore multiple approaches on how to deal with them. You will also use string manipulation techniques to deal with unwanted characters in your dataset.
• You will focus on analyzing the underlying distribution of your data and whether it will impact your machine learning pipeline. You will learn how to deal with skewed data and situations where outliers may be negatively impacting your analysis.
• Finally, you will work with unstructured text data, understanding ways in which you can engineer columnar features out of a text corpus. You will compare how different approaches may impact how much context is being extracted from a text, and how to balance the need for context, without too many features being created.