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.
Author Name: Janani Ravi
Author Description:
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. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  1min
• Exploring scikit-learn for Machine Learning
  43mins
• Understanding the Machine Learning Workflow with scikit-learn
  43mins
• Building a Simple Machine Learning Model with scikit-learn
  38mins