Preparing Data for Modeling with scikit-learn

This course covers important steps in the pre-processing of data, including standardization, normalization, novelty and outlier detection, pre-processing image and text data, as well as explicit kernel approximations such as the RBF and Nystroem methods.
Course info
Level
Advanced
Updated
Aug 12, 2019
Duration
3h 41m
Table of contents
Course Overview
Preparing Numeric Data for Machine Learning
Understanding and Implementing Novelty and Outlier Detection
Preparing Text Data for Machine Learning
Preparing Image Data for Machine Learning
Working with Specialized Datasets
Performing Kernel Approximations
Description
Course info
Level
Advanced
Updated
Aug 12, 2019
Duration
3h 41m
Description

Even as the number of machine learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. Scikit-learn makes the common use-cases in machine learning - clustering, classification, dimensionality reduction and regression - incredibly easy. In this course, Preparing Data for Modeling with scikit-learn, you will gain the ability to appropriately pre-process data, identify outliers and apply kernel approximations. First, you will learn how pre-processing techniques such as standardization and scaling help improve the efficacy of ML algorithms. Next, you will discover how novelty and outlier detection is implemented in scikit-learn. Then, you will understand the typical set of steps needed to work with both text and image data in scikit-learn. Finally, you will round out your knowledge by applying implicit and explicit kernel transformations to transform data into higher dimensions. When you’re finished with this course, you will have the skills and knowledge to identify the correct data pre-processing technique for your use-case and detect outliers using theoretically robust techniques.

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 Modeling with scikit-learn. A little about myself, I have a master's degree in electrical engineering from Stanford, and I've 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. Even as the number of machine learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. Scikit-learn makes the common use cases in machine learning, clustering, classification, dimensionality reduction, and regression, incredibly easy. In this course, you will gain the ability to appropriately pre-process data, identify outliers, and apply kernel approximations. First, you will learn how pre-processing techniques such as standardization and scaling help improve the efficacy of ML algorithms. Next, you will discover how novelty and outlier detection is implemented in scikit-learn. You will then understand the typical set of steps needed to work with both text and image data in scikit-learn. Finally, you'll round out your knowledge by applying implicit and explicit kernel transformations to transform data to higher dimensions. When you're finished with this course, you will have the skills and knowledge to identify the correct data pre-processing technique for your use case, and you'll be able to detect outliers in your dataset using theoretically robust techniques.