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.
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. Topics:
- Course Overview
- Exploring scikit-learn for Machine Learning
- Understanding the Machine Learning Workflow with scikit-learn
- Building a Simple Machine Learning Model with scikit-learn