Machine Learning with Scikit-Learn

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Michael Galarnyk and Madecraft

43:57

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01 - Introduction

01 - Effective machine learning with scikit-learn.mp4

00:54

02 - What you should know before you start.mp4

00:34

03 - Using the exercise files.mp4

00:21

02 - 1. Input and Loading Data

01 - What is machine learning.mp4

01:21

02 - Why use scikit-learn for machine learning.mp4

01:07

03 - 2. Supervised Learning

01 - What is supervised learning.mp4

00:54

02 - How to format data for scikit-learn.mp4

01:55

03 - Linear regression using scikit-learn.mp4

04:32

04 - Train test split.mp4

01:53

05 - Logistic regression using scikit-learn.mp4

03:55

06 - Logistic regression for multiclass classification.mp4

03:36

07 - Decision trees using scikit-learn.mp4

03:09

08 - How to visualize decision trees using Matplotlib.mp4

02:05

09 - Bagged trees using scikit-learn.mp4

02:00

10 - Random forests using scikit-learn.mp4

02:41

11 - Which machine learning model should you use.mp4

01:23

04 - 3. Unsupervised Learning

01 - What is unsupervised learning.mp4

01:15

02 - K-means clustering.mp4

02:28

03 - Principal component analysis (PCA) for data visualization.mp4

02:13

04 - PCA to speed up machine learning algorithms.mp4

02:41

05 - scikit-learn pipelines.mp4

02:05

05 - Conclusion

01 - Get started with scikit-learn.mp4

00:55

Description

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.

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Python

Machine Learning

Artificial Intelligence

Programming Fundamentals

Michael Galarnyk and Madecraft

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