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

Congratulations, you have completed the first module!

## Summary
This module introduced different types of learning algorithms. We learned to use them for data modeling and designing. We also learned some basic principles of linear regression, logistic regression, support vector machines, and decision trees. By the end, we were well-acquainted with tree-based learning algorithms for predicting numeric and categorical outputs.


## What’s next?
Now that we know about machine learning, let’s cover PyCaret. This is necessary to code basic machine learning models in the next module.

Congratulations, you have completed the first module!

# Summary
This module introduced different types of learning algorithms. We learned to use them for data modeling and designing. We also learned some basic principles of linear regression, logistic regression, support vector machines, and decision trees. By the end, we were well-acquainted with tree-based learning algorithms for predicting numeric and categorical outputs.


# What’s next?
Now that we know about machine learning, let’s cover PyCaret. This is necessary to code basic machine learning models in the next module.

Here's a quick summary of the first module for you.

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

Final Remarks

Summary