Preparing Data for Feature Engineering and Machine Learning

By Janani Ravi
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.
Play course overview
Course info
Level
Beginner
Updated
Oct 29, 2019
Duration
3h 18m
Table of contents
Course Overview
Course Overview 2m
Understanding the Role of Features in Machine Learning
Module Overview 1m Prerequisites and Course Outline 1m Features and Labels 6m The Machine Learning Workflow 4m Components of Feature Engineering 3m Feature Selection, Feature Learning, and Feature Extraction 7m Feature Combination and Dimensionality Reduction 4m Training, Validation, and Test Data 6m K-fold Cross Validation 4m Module Summary 1m
Preparing Data for Machine Learning
Module Overview 2m Problems with Data 4m Dealing with Missing Values 5m Dealing with Outliers 6m Applying Different Techniques to Handle Missing Values 7m Detecting and Handling Outliers 7m Reading and Exploring the Dataset 8m Perform Simple and Multiple Linear Regression 4m Module Summary 1m
Understanding and Implementing Feature Selection
Module Overview 2m Types of Data 5m Measuring Correlations 5m Understanding Feature Selection Using Filter, Embedded, and Wrapper Methods 6m Feature Selection Using Missing Value Ratio 5m Calculating and Visualizing Correlations Using Pandas 6m Calculating and Visualizing Correlations Using Yellowbrick 3m Feature Selection Using Filter Methods 6m Feature Selection Using Wrapper Methods 6m Feature Selection Using Embedded Methods 5m Module Summary 2m
Exploring Feature Extraction Techniques
Module Overview 1m Representing Images as Matrices and Image Preprocessing Techniques 5m Feature Detection and Extraction from Images 5m Feature Extraction from Text 6m Module Summary 1m
Implementing Feature Extraction
Module Overview 1m Tokenization and Visualizing Frequency Distributions 4m Performing Normalization Using Different Techniques 5m Creating Feature Vectors from Text Data 6m Loading and Transforming Images 5m Extracting Features from Images 3m Detecting Keypoints and Descriptors to Perform Image Matching 5m Extracting Text from Images Using OCR 4m Extracting Features from Dates 5m Working with Geospatial Features 7m Summary and Further Study 2m
Description
Course info
Level
Beginner
Updated
Oct 29, 2019
Duration
3h 18m
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
Janani Ravi
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.

More from the author
Building Statistical Summaries with R
Advanced
3h 3m
Jan 1, 2020
Understanding Statistical Models and Mathematical Models
Beginner
2h 35m
Dec 17, 2019
Interpreting Data Using Descriptive Statistics with Python
Intermediate
2h 21m
Nov 8, 2019
More courses by Janani Ravi
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.

You're the smartest person in the room

PROVE IT

Your team won't just close the skills gap

THEY'LL HURDLE IT