English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 26m | 355 MB
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.
What you’ll learn
- What is feature engineering?
- Exploring the data
- Plotting features
- Cleaning existing features
- Creating new features
- Standardizing features
- Comparing the impacts on model performance
Table of Contents
Introduction
1 The secret of effective machine learning
2 What you should know
3 What tools you need
4 Using the exercise files
Review Machine Learning Basics
5 What is machine learning
6 What does machine learning look like in real life
7 What does an end-to-end machine learning pipeline look like
Introduction to Feature Engineering
8 What is feature engineering
9 Why does feature engineering matter
10 What are the tools in the feature engineering toolbox
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
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
Prepare Features for Modeling
24 Create training and test sets
25 Standardize all features
26 Write out three final datasets
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
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