Preparing Data for Feature Engineering and Machine Learning

This course covers categories of feature engineering techniques used to get the best results from a machine learning model, including feature selection, and several feature extraction techniques to re-express features in the most appropriate form.
Course info
Rating
(13)
Level
Beginner
Updated
Oct 29, 2019
Duration
3h 17m
Table of contents
Course Overview
Understanding the Role of Features in Machine Learning
Preparing Data for Machine Learning
Understanding and Implementing Feature Selection
Exploring Feature Extraction Techniques
Implementing Feature Extraction
Description
Course info
Rating
(13)
Level
Beginner
Updated
Oct 29, 2019
Duration
3h 17m
Description

However well designed and well implemented a machine learning model is, if the data fed in is poorly engineered, the model’s predictions will be disappointing.

In this course, Preparing Data for Feature Engineering and Machine Learning, you will gain the ability to appropriately pre-process your data -- in effect engineer it -- so that you can get the best out of your ML models.

First, you will learn how feature selection techniques can be used to find predictors that contain the most information. Feature selection can be broadly grouped into three categories known as filter, wrapper, and embedded techniques and we will understand and implement all of these.

Next, you will discover how feature extraction differs from feature selection, in that data is substantially re-expressed, sometimes in forms that are hard to interpret. You will then understand techniques for feature extraction from image and text data.

Finally, you will round out your knowledge by understanding how to leverage powerful Python libraries for working with images, text, dates, and geo-spatial data.

When you’re finished with this course, you will have the skills and knowledge to identify the correct feature engineering techniques, and the appropriate solutions for your use-case.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on Preparing Data for Feature Engineering and Machine Learning. A little about myself, I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real time collaborative editing in Google Docs and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high quality video content. However well designed and well implemented a machine learning model is, if the data fed in is poorly engineered, the model's predictions will be disappointing. In this course, you will gain the ability to appropriately pre-process your data, in effect engineer it so that you can get the best out of your ML models. First, you will learn how feature selection techniques can be used to find predictors that contain the most information. Feature selection can be broadly grouped into three categories known as filter, wrapper, and embedded techniques, and we will understand and implement all of these. Next, you will discover how feature extraction defers from feature selection in that data is substantially re-expressed, sometimes in forms that are hard to interpret. You will then understand techniques for feature extraction from image and text data. Finally, you will round out your knowledge by understanding how to leverage powerful Python libraries for working with images, text, dates, and geospatial data. When you're finished with this course, you will have the skills and knowledge to identify the correct feature engineering techniques and the appropriate solutions for your use case.