Machine Learning with Scikit-Learn

Machine Learning with Scikit-Learn

The ability to apply machine learning algorithms is an important part of a data scientist’s skill set. scikit-learn is a popular open-source Python library that offers user-friendly and efficient versions of common machine learning algorithms. In this course, data scientist Michael Galarnyk explains how to use scikit-learn for supervised and unsupervised machine learning. Michael reviews the benefits of this easy-to-use API and then quickly segues to practical techniques, starting with linear and logistic regression, decision trees, and random forest models. In chapter three, he covers unsupervised learning techniques such as K-means clustering and principal component analysis (PCA). Plus, learn how to create scikit-learn pipelines to make your code cleaner and more resilient to bugs. By the end of the course, you'll be able to understand the strengths and weaknesses of each scikit-learn algorithm and build better, more efficient machine learning models.

This course was created by Madecraft. We are pleased to host this content in our library.

Topics include:

  • Why use scikit-learn?
  • Supervised vs. unsupervised learning
  • Linear and logistic regression
  • Decision trees and random forests
  • K-means clustering
  • Principal component analysis (PCA)

课程信息

  • 英文名称:Machine Learning with Scikit-Learn
  • 时长:43分57秒
  • 字幕:英语

课程目录

  1. Effective machine learning with scikit-learn
  2. What you should know before you start
  3. Using the exercise files
  4. What is machine learning?
  5. Why use scikit-learn for machine learning?
  6. What is supervised learning?
  7. How to format data for scikit-learn
  8. Linear regression using scikit-learn
  9. Train test split
  10. Logistic regression using scikit-learn
  11. Logistic regression for multiclass classification
  12. Decision trees using scikit-learn
  13. How to visualize decision trees using Matplotlib
  14. Bagged trees using scikit-learn
  15. Random forests using scikit-learn
  16. Which machine learning model should you use?
  17. What is unsupervised learning?
  18. K-means clustering
  19. Principal component analysis (PCA) for data visualization
  20. PCA to speed up machine learning algorithms
  21. scikit-learn pipelines
  22. Get started with scikit-learn

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