https://github.com/ajaymache/machine-learning-yearning

Machine Learning Yearning book by 🅰️𝓷𝓭𝓻𝓮𝔀 🆖
https://github.com/ajaymache/machine-learning-yearning

andrew-ng-machine-learning andrew-ng-machine-learning-yearning deep-learning deep-learning-andrew-ng deeplearning-ai machine-learning machine-learning-coursera machine-learning-yearning

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Machine Learning Yearning book by 🅰️𝓷𝓭𝓻𝓮𝔀 🆖

• Host: GitHub
• URL: https://github.com/ajaymache/machine-learning-yearning
• Owner: ajaymache
• License: mit
• Created: 2018-04-17T19:12:29.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-12-20T02:08:09.000Z (almost 6 years ago)
• Last Synced: 2023-11-07T13:21:01.346Z (about 1 year ago)
• Topics: andrew-ng-machine-learning, andrew-ng-machine-learning-yearning, deep-learning, deep-learning-andrew-ng, deeplearning-ai, machine-learning, machine-learning-coursera, machine-learning-yearning
• Homepage:
• Size: 9.27 MB
• Stars: 931
• Watchers: 49
• Forks: 271
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        


# Machine Learning Yearning  [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Machine%20Learning%20Yearning%2C%20book%20by%20Andrew%20Ng%20&url=https://github.com/ajaymache/machine-learning-yearning&hashtags=machinelearning,ML,AI,deeplearning,andrewng,coursera,datascience)  ![license](/shields/license-free-orange.svg)  [![GitHub issues](https://img.shields.io/github/issues/ajaymache/travis-ci-with-github.svg?colorB=99cc33)](https://github.com/ajaymache/travis-ci-with-github/issues)  ![Contributions](/shields/contributions.svg)

#### Technical Strategy for AI Engineers, In the Era of Deep Learning

Author : 🅰️𝓷𝓭𝓻𝓮𝔀 🆖

### About

The book has been divided into 13 parts originally by _**Prof. Andrew NG**_ along with the complete book with all the parts consolidated. In this book you will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. Recommendations for how to set up dev/test sets have been changing as Machine Learning is moving toward bigger datasets, and this explains how you should do it for modern ML projects.

### Contents

:zero::zero: [Full Book](/full%20book/machine-learning-yearning.pdf)  

:zero::one: [Chapters 1 to 14](machine-learning-yearning-part1.pdf)

:zero::two: [Chapters 15 to 19](machine-learning-yearning-part2.pdf)

:zero::three: [Chapters 20 to 22](machine-learning-yearning-part3.pdf)

:zero::four: [Chapters 23 to 27](machine-learning-yearning-part4.pdf)

:zero::five: [Chapters 28 to 30](machine-learning-yearning-part5.pdf)

:zero::six: [Chapters 31 to 32](machine-learning-yearning-part6.pdf)

:zero::seven: [Chapters 33 to 35](machine-learning-yearning-part7.pdf)

:zero::eight: [Chapters 36 to 39](machine-learning-yearning-part8.pdf)

:zero::nine: [Chapters 40 to 43](machine-learning-yearning-part9.pdf)

:one::zero: [Chapters 44 to 46](machine-learning-yearning-part10.pdf)

:one::one: [Chapters 47 to 49](machine-learning-yearning-part11.pdf)

:one::two: [Chapters 50 to 52](machine-learning-yearning-part12.pdf)

:one::three: [Chapters 53 to 58](machine-learning-yearning-part13.pdf)