Building Your First scikit-learn Solution

This course covers both the why and how of using scikit-learn. You'll delve into scikit-learn’s niche in the ever-growing taxonomy of machine learning libraries, and important aspects of working with scikit-learn estimators and pipelines.
Course info
Level
Beginner
Updated
May 2, 2019
Duration
2h 7m
Table of contents
Description
Course info
Level
Beginner
Updated
May 2, 2019
Duration
2h 7m
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, Building Your First scikit-learn Solution, you'll gain the ability to identify the situations where scikit-learn is exactly the tool you are looking for, and also those situations where you need something else. First, you'll learn how scikit-learn’s niche is traditional machine learning, as opposed to deep learning or building neural networks. Next, you'll discover how seamlessly it integrates with core Python libraries. Then, you'll explore the typical set of steps needed to work with models in scikit-learn. Finally, you'll round out your knowledge by building your first scikit-learn regression and classification models. When you’re finished with this course, you'll have the skills and knowledge to identify precisely the situations when scikit-learn ought to be your tool of choice, and also how best to leverage the formidable capabilities of scikit-learn.

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 Building Your First scikit-learn Solution. 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. In this course, you will gain the ability to identify the situations where scikit-learn is exactly the tool you are looking for, and also those situations where you need something else. First, you will learn how scikit-learn's niche is traditional machine learning, as opposed to deep learning or building neural networks. Next, you will discover how seamlessly scikit-learn integrates with core Python libraries. You will then understand the typical set of steps needed to work with models in scikit-learn. Finally, you will round out your knowledge by building your first scikit-learn regression and classification models. When you're finished with this course, you will have the skills and knowledge to identify precisely the situations when scikit-learn ought to be your tool of choice, and also how best to leverage the formidable capabilities of scikit-learn.