# Introduction to Machine Learning

> Google에서 제공하는 무료 기계학습 입문 과정.

[Google](https://wiki.g15e.com/pages/Google.txt)에서 제공하는 무료 [기계학습](https://wiki.g15e.com/pages/Machine%20learning.txt) 입문 과정.

https://developers.google.com/machine-learning/intro-to-ml

## Learning objectives

- Understand the different types of machine learning.
- Understand the key concepts of supervised machine learning.
- Learn how solving problems with ML is different from traditional approaches.

## Definition of [ML](https://wiki.g15e.com/pages/Machine%20learning.txt)

> In basic terms, ML is the process of [training](https://wiki.g15e.com/pages/Training%20(machine%20learning.txt)) a piece of software, called a [model](https://wiki.g15e.com/pages/Model%20(machine%20learning.txt)), to make useful [predictions](https://wiki.g15e.com/pages/Prediction%20(machine%20learning.txt)) or generate content from data.

## Types of ML Systems

- [Supervised learning](https://wiki.g15e.com/pages/Supervised%20learning.txt)
  - [Regression model](https://wiki.g15e.com/pages/Regression%20model.txt)
    - [[Linear regression]]
    - [[Logistic regression]]
  - [Classification model](https://wiki.g15e.com/pages/Classification%20model.txt)
    - [[Binary classification]]
    - [[Multi-class classification]]
- <Unsupervised learning>
- [Reinforcement learning](https://wiki.g15e.com/pages/Reinforcement%20learning.txt)
- [Generative AI](https://wiki.g15e.com/pages/Generative%20AI.txt)

## What's Next

- [Introduction to machine learning problem framing](https://wiki.g15e.com/pages/Introduction%20to%20machine%20learning%20problem%20framing.txt): If you're looking for a field-tested approach for creating ML models and avoiding common pitfalls along the way.
- [People + AI guidebook](https://wiki.g15e.com/pages/People%20+%20AI%20guidebook.txt): If you're looking for a set of methods, best practices and examples presented by Googlers, industry experts, and academic research for using ML.
- [Machine learning crash course](https://wiki.g15e.com/pages/Machine%20learning%20crash%20course.txt): If you're ready for an in-depth and hands-on approach to learning more about ML.