The ability to apply machine learning algorithms is an important part of a data scientist’s skill set. But with so many options to choose from, it can be hard to know which tools you should use. scikit-learn is a popular open-source Python library that offers user-friendly and efficient versions of common machine learning algorithms. In this course, learn how to use scikit-learn for supervised and unsupervised machine learning.
Explore the benefits of this powerful API and practical techniques for using it, including linear and logistic regression, decision trees, and random forest models, as well as unsupervised learning techniques such as K-means clustering and principal component analysis (PCA). Along the way, learn how to create scikit-learn pipelines to make your code cleaner and more resilient to bugs. By the end of this course, you’ll be prepared to leverage the strengths and weaknesses of each scikit-learn algorithm and build better, more efficient machine learning models.
