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《machine-learning-mindmap》 机器学习思维导图 |
《machine-learning-mindmap》
机器学习思维导图
Daniel Martinez
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摘要 : A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
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作者：Daniel Martinez
Senior Systems Engineer and Data Scientist at Splunk
About Me
Twitter:
https://twitter.com/danielmartinezf
Linkedin:
https://www.linkedin.com/in/danielmartinezformoso/
Email:
daniel.martinez.formoso@gmail.com
待翻译编辑:
zhimap.com
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Overview
Machine Learning is a sub field of computer science that gives computers the ability to learn without being explicitly programmed.
It explores the study and construction of algorithms that can learn from and make predictions on data.
机器学习是计算机科学的一个子领域，可使计算机无需具体编程就能学习某种能力。
主要研究通过学习和算法对已有数据进行预测能力。
Machine Learning is as fascinating as it is broad in scope.
It spans over multiple fields in Mathematics, Computer Science, and Neuroscience.
This is an attempt to summarize this enormous field in one .PDF file.
1. Process
The Data Science it's not a set-and-forget effort, but a process that requires design, implementation and maintenance.
The PDF contains a quick overview of what's involved. Here's a quick screenshot.
2. Data Processing
First, we'll need some data. We must find it, collect it, clean it, and about 5 other steps. Here's a sample of what's required.
3. Mathematics
Machine Learning is a house built on Math bricks. Browse through the most common components, and send your feedback if you see something missing.
4. Concepts
A partial list of the types, categories, approaches, libraries, and methodology.
5. Models
A sampling of the most popular models. Send your comments to add more.