Machine Learning with Scikit-Learn (2020)

44mAdvanced2020-10-15

Authors

Michael Galarnyk

Python Instructor and Blogger

Madecraft

Full-Service Learning Content Company

Course details

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)

Skills covered

scikit-learnMachine LearningEssential TrainingArtificial Intelligence (AI)Open Source

Concepts

0. Introduction

01 - Effective machine learning with scikit-learn
02 - What you should know before you start
03 - Using the exercise files

1. Input and Loading Data

04 - What is machine learning
05 - Why use scikit-learn for machine learning

2. Supervised Learning

06 - What is supervised learning
07 - How to format data for scikit-learn
08 - Linear regression using scikit-learn
09 - 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

3. Unsupervised Learning

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

Conclusion

22 - Get started with scikit-learn