Learn what machine learning is and get a high-level view of it with Python.

Machine Learning with Python Libraries

Machine learning is a type of artificial intelligence used with multiple Python libraries. In this module, we’ll learn to code basic machine learning models, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. This module will teach you how to import datasets into Python using pandas. We’ll also learn about tree-based learning algorithms to predict numeric and categorical outputs. If you need to revise the concepts, we have summarized key concepts of machine learning, and the overviews of specific algorithms are dispersed throughout the module.

A Practical Guide to Machine Learning with Python

The distance between the boundary line and the nearest data point


Support vector machines can be used for:

Support vector machines are more effective at classifying multiple discrete outcomes than logistic regression. True or False?

Support vector machines are more effective at classifying multiple discrete outcomes than logistic regression. True or False?

What is the name of the classification version of support vector machines in Scikit-learn?

What is the name of the classification version of support vector machines in Scikit-learn?

Ignoring data points that cross over into the margin


No leeway for data points that cross over into the margin


What is a feature of a soft margin?

In general, a small gamma produces:

Before We Begin

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Unsupervised Learning

Deep Learning

Conclusion

Appendix

Quiz