Applied Machine Learning: Feature Engineering (2020)

Course details

The quality of the predictions coming out of your machine learning model is a direct reflection of the data you feed it during training. Feature engineering helps you extract every last bit of value out of data. This course provides the tools to take a data set, tease out the signal, and throw out the noise in order to optimize your models. The concepts generalize to nearly any kind of machine learning algorithm. Instructor Derek Jedamski provides a refresher on machine learning basics and a thorough introduction to feature engineering. He explores continuous and categorical features and shows how to clean, normalize, and alter them. Learn how to address missing values, remove outliers, transform data, create indicators, and convert features. In the final chapters, Derek explains how to prepare features for modeling and provides four variations for comparison, so you can evaluate the impact of cleaning, transforming, and creating features through the lens of model performance.

Topics include:
What is feature engineering?
Exploring the data
Plotting features
Cleaning existing features
Creating new features
Standardizing features
Comparing the impacts on model performance

Concepts

0. Introduction

01 - The secret of effective machine learning
02 - What you should know
03 - What tools you need
04 - Using the exercise files

1. Review Machine Learning Basics

05 - What is machine learning
06 - What does machine learning look like in real life
07 - What does an end-to-end machine learning pipeline look like

2. Introduction to Feature Engineering

08 - What is feature engineering
09 - Why does feature engineering matter
10 - What are the tools in the feature engineering toolbox

3. Explore the Data

11 - What data are you using
12 - Explore continuous features
13 - Plot continuous features
14 - Explore categorical features
15 - Plot categorical features
16 - Summary of features

4. Creating and Cleaning Features

17 - Treat missing values in the data
18 - Cap and floor data to remove outliers
19 - Transform skewed features
20 - Creating new features from text
21 - Create indicators
22 - Combining existing features into a new feature
23 - Convert categorical features to numeric

5. Prepare Features for Modeling

24 - Create training and test sets
25 - Standardize all features
26 - Write out three final datasets

6. Compare All Features

27 - Review model evaluation basics
28 - Build a model with raw original features
29 - Build a model with cleaned original features
30 - Build a model with all features
31 - Build a model with reduced set of features
32 - Compare and evaluate all model variations

Conclusion

33 - How to continue advancing your skills

Applied Machine Learning: Feature Engineering (2020)

Authors

Derek Jedamski