《machine-learning-mindmap》
机器学习思维导图
Daniel Martinez

• Source
  来源

  • https://github.com/dformoso/machine-learning-mindmap

  • 摘要 : A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

  • LICENSE

    • Apache License 2.0

  • 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

      可关注微信公众号

      手机上编辑、浏览

      
      

      
      

      
      

• 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.