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Course
- Data
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.
What you'll learn
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.
Table of contents
- Version Check | 16s
- Module Overview | 1m 4s
- Prerequisites and Course Outline | 1m 8s
- Introducing Machine Learning | 3m 51s
- Learning from Data: Training and Prediction | 6m 11s
- Traditional and Representation ML Models | 7m 4s
- The Niche of scikit-learn in ML | 5m 22s
- Exploring scikit-learn Libraries | 6m 39s
- Supervised and Unsupervised Learning | 7m 6s
- Installing scikit-learn Libraries | 3m 42s
- Summary | 1m 19s
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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