msb-public
/
ML-For-Beginners
mirror of https://github.com/microsoft/ML-For-Beginners.git
1
0
Code Issues Projects Releases Wiki Activity

Update README.md

Browse Source
pull/34/head
Jen Looper 4 years ago committed by GitHub
parent 354df4df0f
commit 8bd8bd2be8
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 8 additions and 8 deletions
Show Stats Download Patch File Download Diff File

16
README.md
Unescape View File

@ -12,7 +12,7 @@
> 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍
Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Machine Learning. Travel with us around the world as we apply these techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about traditional Machine Learning. In this lesson group, you will learn about what is sometimes called 'classic' ML, using primarily Scikit-Learn as a library and avoiding deep learning, which is covered in our 'AI for Beginners' curriculum. Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
**Hearty thanks to our authors (list all authors here)**
@ -37,7 +37,7 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson cur
## Pedagogy
We have chosen two pedagogical tenets while building this curriculum: ensuring that it is **project-based** and that it includes **frequent quizzes**. In addition, this curriculum has a common **theme** to give it cohesion.
We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on **project-based** and that it includes **frequent quizzes**. In addition, this curriculum has a common **theme** to give it cohesion.
By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12 week cycle.
@ -63,25 +63,25 @@ By ensuring that the content aligns with projects, the process is made more enga
| :-----------: | :--------------------------------------: | :----------------------------------: | ---------------------------------------------------------------------------------------------------- | :------------------------------------------------: | :-------: |
| 01 | [Introduction](1-Introduction/README.md) | Introduction to Machine Learning | Learn the basic concepts behind Machine Learning | [lesson](1-Introduction/1-intro-to-ML/README.md) | Amy |
| 02 | [Introduction](1-Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](1-Introduction/2-history-of-ML/README.md) | Amy |
| 03 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Get started with Python and Scikit-Learn for Regression models | [lesson](2-Regression/1-Tools/README.md) | Jen |
| 03 | Introduction to Regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-Learn for Regression models | [lesson](2-Regression/1-Tools/README.md) | Jen |
| 04 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](2-Regression/2-Data/README.md) | Jen |
| 05 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build a Linear Regression model | [lesson](2-Regression/3-Linear/README.md) | Jen |
| 06 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build a Logistic Regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen |
| 07 | Delicious Asian Recipes 🍜 | [Clustering]() | tbd | [lesson]() | |
| 07 | Introduction to Clustering | [Clustering]() | tbd | [lesson]() | |
| 08 | Delicious Asian Recipes 🍜 | [Clustering]() | tbd | [lesson]() | |
| 09 | Delicious Asian Recipes 🍜 | [Clustering]() | tbd | [lesson]() | |
| 10 | Delicious Asian Recipes 🍜 | [Clustering]() | tbd | [lesson]() | |
| 11 | Interesting Maya Architecture 🦜 | [Classification]() | tbd | [lesson]() | |
| 11 | Introduction to Classification | [Classification]() | tbd | [lesson]() | |
| 12 | Interesting Maya Architecture 🦜 | [Classification]() | tbd | [lesson]() | |
| 13 | Interesting Maya Architecture 🦜 | [Classification]() | tbd | [lesson]() | |
| 14 | Interesting Maya Architecture 🦜 | [Classification]() | tbd | [lesson]() | |
| 15 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 15 | Introduction to NLP | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 16 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 17 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 18 | Romantic Hotels of Europe ♥️ | [Natural Language Processing]() | tbd | [lesson]() | Stephen |
| 19 | Power Usage in India ⚡️ | [Time Series]() | tbd | [lesson]() | Francesca |
| 19 | Introduction to Time Series Forecasting | [Time Series]() | tbd | [lesson]() | Francesca |
| 20 | Power Usage in India ⚡️ | [Time Series]() | tbd | [lesson]() | Francesca |
| 21 | A fancy French mousetrap 🍫 | [Reinforcement Learning]() | tbd | [lesson]() | |
| 21 | Introduction to Reinforcement Learning | [Reinforcement Learning]() | tbd | [lesson]() | |
| 22 | A fancy French mousetrap 🍫 | [Reinforcement Learning]() | tbd | [lesson]() | |
| 23 | Future | The Ethics of Machine Learning | What are the important ethical issues apparent now and how will they impact the field going forward? | [lesson](8-Future/Ethics/README.md) | Tomomi |
| 24 | Future | The Future of Machine Learning | What are the important trends that will shape the future of ML? | [lesson](8-Future/Future-Trends/README.md) | |

Write Preview
Loading…
Cancel
Save