@ -47,7 +47,7 @@ Although the terms can be confused, machine learning (ML) is an important subset
In this curriculum, we are going to cover only the core concepts of machine learning that a beginner must know. We cover what we call 'Classical machine learning' primarily using Scikit-learn, an excellent library many students use to learn the basics. To understand broader concepts of artificial intelligence or deep learning, a strong fundamental knowledge of machine learning is indispensable, and so we would like to offer it here.
You will additionally learn the basics of Regression, Classification, Clustering, Natural Language Processing, Time Series Forecasting, and Reinforcement Learning, as well as real-world applications, the history of ML, ML and Fairness, and how to use your model in web apps.
You will additionally learn the basics of Regression, Classification, Clustering, natural language processing, Time Series Forecasting, and Reinforcement Learning, as well as real-world applications, the history of ML, ML and Fairness, and how to use your model in web apps.
@ -38,7 +38,7 @@ The workshop is credited with having initiated and encouraged several discussion
From the 1950s through the mid '70s, optimism ran high in the hope that AI could solve many problems. In 1967, Marvin Minsky stated confidently that "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
Natural Language Processing research flourished, search was refined and made more powerful, and the concept of 'micro-worlds' was created, where simple tasks were completed using plain language instructions.
natural language processing research flourished, search was refined and made more powerful, and the concept of 'micro-worlds' was created, where simple tasks were completed using plain language instructions.
Research was well funded by government agencies, advances were made in computation and algorithms, and prototypes of intelligent machines were built. Some of these machines include:
@ -13,7 +13,7 @@ NLP, as it is commonly known, is one of the best-known areas where machine learn
You will learn about how the ideas about languages developed and what the major areas of study have been. You will also learn definitions and concepts about how computers process text, including parsing, grammar, and identifying nouns and verbs. There are some coding tasks in this lesson, and several important concepts are introduced that you will learn to code later on in the next lessons.
Computational linguistics is an area of research and development over many decades that studies how computers can work with, and even understand, translate, and communicate with languages. Natural Language Processing (NLP) is a related field focused on how computers can process 'natural', or human, languages. If you have ever dictated to your phone instead of typing or asked a virtual assistant a question, your speech was converted into a text form and then processed or *parsed* from the language you spoke. The detected keywords were then processed into a format that the phone or assistant could understand and act on.
Computational linguistics is an area of research and development over many decades that studies how computers can work with, and even understand, translate, and communicate with languages. natural language processing (NLP) is a related field focused on how computers can process 'natural', or human, languages. If you have ever dictated to your phone instead of typing or asked a virtual assistant a question, your speech was converted into a text form and then processed or *parsed* from the language you spoke. The detected keywords were then processed into a format that the phone or assistant could understand and act on.
This is possible because someone wrote a computer program to do this. A few decades ago, some science fiction writers predicted that people would mostly speak to their computers, and the computers would always understand exactly what they meant. Sadly, it turned out to be a harder problem that many imagined, and while it is a much better understood problem today, there are significant challenges in achieving 'perfect' natural language processing when it comes to understanding the meaning of a sentence. This is a particularly hard problem when it comes to understanding humour or detecting emotions such as sarcasm in a sentence.
# Common Natural Language Processing Tasks and Techniques
# Common natural language processing Tasks and Techniques
For most *Natural Language Processing* tasks, the text to be processed must be broken down, examined, and the results stored or cross referenced with rules and data sets. This allows the programmer to derive the meaning or intent or only the frequency of terms and words in a text.
For most *natural language processing* tasks, the text to be processed must be broken down, examined, and the results stored or cross referenced with rules and data sets. This allows the programmer to derive the meaning or intent or only the frequency of terms and words in a text.
# Getting Started with Natural Language Processing
# Getting Started with natural language processing
## Regional topic: European literature and Romantic Hotels of Europe ❤️
In this section of the curriculum, you will be introduced to one of the most widespread uses of machine learning: Natural Language Processing (NLP). Derived from Computational Linguistics, this category of artificial intelligence is the bridge between humans and machines via voice or textual communication.
In this section of the curriculum, you will be introduced to one of the most widespread uses of machine learning: natural language processing (NLP). Derived from Computational Linguistics, this category of artificial intelligence is the bridge between humans and machines via voice or textual communication.
In these lessons we'll learn the basics of NLP by building small conversational bots to learn how Machine Learning aids in making these conversations more and more 'smart'. You'll travel back in time, chatting with Elizabeth Bennett and Mr. Darcy from Jane Austen's classic novel, **Pride and Prejudice**, published in 1813. Then, you'll further your knowledge by learning about sentiment analysis via hotel reviews in Europe.
@ -11,7 +11,7 @@ In these lessons we'll learn the basics of NLP by building small conversational
## Lessons
1. [Introduction to Natural Language Processing](1-Introduction-to-NLP/README.md)
1. [Introduction to natural language processing](1-Introduction-to-NLP/README.md)
2. [Common NLP Tasks and Techniques](2-Tasks/README.md)
3. [Translation and Sentiment Analysis with Machine Learning](3-Translation-Sentiment/README.md)
4. TBD
@ -19,4 +19,4 @@ In these lessons we'll learn the basics of NLP by building small conversational
## Credits
These Natural Language Processing lessons were written with ☕ by [Stephen Howell]([Twitter](https://twitter.com/Howell_MSFT))
These natural language processing lessons were written with ☕ by [Stephen Howell]([Twitter](https://twitter.com/Howell_MSFT))
In this curriculum, you have learned many ways to prepare data for training and create machine learning models. You built a series of classic Regression, Clustering, Classification, Natural Language Processing, and Time Series models. Congratulations! Now, you might be wondering what it's all for... what are the real world applications for these models?
In this curriculum, you have learned many ways to prepare data for training and create machine learning models. You built a series of classic Regression, Clustering, Classification, natural language processing, and Time Series models. Congratulations! Now, you might be wondering what it's all for... what are the real world applications for these models?
While a lot of interest in industry has been garnered by AI, which usually leverages deep learning, there are still valuable applications for classical machine learning models. You might even use some of these applications today! In this lesson, you'll explore how eight different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and valuable to users.
> 🌍 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 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 forthcoming 'AI for Beginners' curriculum.
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 forthcoming 'AI for Beginners' curriculum. Pair this curriculum with our forthcoming 'Data Science for Beginners' curriculum, as well!
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'.
@ -69,34 +69,38 @@ By ensuring that the content aligns with projects, the process is made more enga
> **A note about quizzes**: All quizzes are contained [in this app](https://jolly-sea-0a877260f.azurestaticapps.net), for 48 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
| 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) | Muhammad |
| 02 | [Introduction](1-Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | [Introduction](1-Introduction/README.md) | Fairness and Machine Learning | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [lesson](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | [Introduction](1-Introduction/README.md) | Techniques for Machine Learning | What techniques do ML researchers use to build ML models? | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | 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 |
| 06 | 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 |
| 07 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build Linear and Polynomial Regression models | [lesson](2-Regression/3-Linear/README.md) | Jen |
| 08 | North American Pumpkin Prices 🎃 | [Regression](2-Regression/README.md) | Build a Logistic Regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen |
| 09 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a Web app to use your trained model | [lesson](3-Web-App/README.md) | Jen |
| 10 | Introduction to Classification | [Classification](4-Classification/README.md) | Clean, Prep, and Visualize your Data; Introduction to Classification | [lesson](4-Classification/1-Introduction/README.md) | Jen and Cassie |
| 11 | Delicious Asian and Indian Cuisines 🍜 | [Classification](4-Classification/README.md) | Introduction to Classifiers | [lesson](4-Classification/2-Classifiers-1/README.md) | Jen and Cassie |
| 12 | Delicious Asian and Indian Cuisines 🍜 | [Classification](4-Classification/README.md) | More Classifiers | [lesson](4-Classification/3-Classifiers-2/README.md) | Jen and Cassie |
| 13 | Delicious Asian and Indian Cuisines 🍜 | [Classification](4-Classification/README.md) | Build a Recommender Web App using your Model | [lesson](4-Classification/4-Applied/README.md) | Jen |
| 14 | Introduction to Clustering | [Clustering](5-Clustering/README.md) | Clean, Prep, and Visualize your Data; Introduction to Clustering | [lesson](5-Clustering/1-Visualize/README.md) | Jen |
| 16 | Introduction to Natural Language Processing ☕️ | [Natural Language Processing](6-NLP/README.md) | Learn the basics about NLP by building a simple bot | [lesson](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Common NLP Tasks ☕️ | [Natural Language Processing](6-NLP/README.md) | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures | [lesson](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Translation and Sentiment Analysis ♥️ | [Natural Language Processing](6-NLP/README.md) | Translation and Sentiment analysis with Jane Austen | [lesson](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantic Hotels of Europe ♥️ | [Natural Language Processing](6-NLP/README.md) | Sentiment analysis, continued | [lesson]() | Stephen |
| 20 | Introduction to Time Series Forecasting | [Time Series](7-TimeSeries/README.md) | Introduction to Time Series Forecasting | [lesson](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 21 | ⚡️ World Power Usage ⚡️ Time Series Forecasting with ARIMA ⚡️ | [Time Series](7-TimeSeries/README.md) | Time Series Forecasting with ARIMA | [lesson](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 22 | Introduction to Reinforcement Learning | [Reinforcement Learning](8-Reinforcement/README.md) | Introduction to Reinforcement Learning with Q-Learning | [lesson](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 23 | Help Peter avoid the Wolf! 🐺 | [Reinforcement Learning](8-Reinforcement/README.md) | Reinforcement Learning Gym | [lesson](8-Reinforcement/2-Gym/README.md) | Dmitry |
| 24 | Real-World ML Scenarios and Applications | [ML in the Wild](9-Real-World/README.md) | Interesting and Revealing real-world applications of classical ML | [lesson](9-Real-World/1-Applications/README.md) | Team |
| 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) | Muhammad |
| 02 | [Introduction](1-Introduction/README.md) | The History of machine learning | Learn the history underlying this field | [lesson](Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | [Introduction](1-Introduction/README.md) | Fairness and machine learning | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [lesson](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | [Introduction](1-Introduction/README.md) | Techniques for machine learning | What techniques do ML researchers use to build ML models? | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | 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 |
| 06 | 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 |
| 07 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build linear and polynomial regression models | [lesson](2-Regression/3-Linear/README.md) | Jen |
| 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic Regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen |
| 09 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a web app to use your trained model | [lesson](3-Web-App/README.md) | Jen |
| 10 | Introduction to classification | [Classification](4-Classification/README.md) | Clean, prep, and visualize your data; introduction to classification | [lesson](4-Classification/1-Introduction/README.md) | Jen and Cassie |
| 11 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Introduction to classifiers | [lesson](4-Classification/2-Classifiers-1/README.md) | Jen and Cassie |
| 12 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | More classifiers | [lesson](4-Classification/3-Classifiers-2/README.md) | Jen and Cassie |
| 13 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Build a recommender web app using your model | [lesson](4-Classification/4-Applied/README.md) | Jen |
| 14 | Introduction to clustering | [Clustering](5-Clustering/README.md) | Clean, prep, and visualize your data; Introduction to clustering | [lesson](5-Clustering/1-Visualize/README.md) | Jen |
| 16 | Introduction to natural language processing ☕️ | [Natural language processing](6-NLP/README.md) | Learn the basics about NLP by building a simple bot | [lesson](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Common NLP Tasks ☕️ | [Natural language processing](6-NLP/README.md) | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures | [lesson](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Translation and sentiment analysis ♥️ | [Natural language processing](6-NLP/README.md) | Translation and sentiment analysis with Jane Austen | [lesson](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis, continued | [lesson]() | Stephen |
| 20 | Introduction to time series forecasting | [Time series](7-TimeSeries/README.md) | Introduction to time series forecasting | [lesson](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 21 | ⚡️ World Power Usage ⚡️ Time series forecasting with ARIMA | [Time series](7-TimeSeries/README.md) | Time series forecasting with ARIMA | [lesson](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 22 | Introduction to reinforcement learning | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning | [lesson](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 23 | Help Peter avoid the wolf! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym | [lesson](8-Reinforcement/2-Gym/README.md) | Dmitry |
| 24 | Real-World ML scenarios and applications | [ML in the Wild](9-Real-World/README.md) | Interesting and revealing real-world applications of classical ML | [lesson](9-Real-World/1-Applications/README.md) | Team |
## Offline access
You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
## PDFs
Find a pdf of the curriculum with links [here](pdf/readme.pdf)
Jen Looper
4 years ago