@ -7,11 +7,10 @@ In this section of the curriculum, you will be introduced to the base concepts u
### Lessons
1. [Introduction to Machine Learning](1-intro-to-ML/README.md)
1. [The History of Machine Learning and AI](2-history-of-ML/README.md)
1. [Fairness and Machine Learning](3-fairness/README.md)
1. [Techniques of Machine Learning](4-techniques-of-ML/README.md)
1. [Introduction to machine learning](1-intro-to-ML/README.md)
1. [The History of machine learning and AI](2-history-of-ML/README.md)
1. [Fairness and machine learning](3-fairness/README.md)
1. [Techniques of machine learning](4-techniques-of-ML/README.md)
### Credits
"Introduction to Machine Learning" was written with ♥️ by a team of folks including [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) and [Jen Looper](https://twitter.com/jenlooper)
## Regional topic: Regression models for pumpkin prices in North America 🎃
In North America, pumpkins are often carved into scary faces for Halloween. Let's discover more about these fascinating vegetables!
@ -8,25 +8,25 @@ In North America, pumpkins are often carved into scary faces for Halloween. Let'
## What you will learn
The lessons in this section cover types of Regression in the context of machine learning. Regression models can help determine the _relationship_ between variables. This type of model can predict values such as length, temperature, or age, thus uncovering relationships between variables as it analyzes data points.
The lessons in this section cover types of regression in the context of machine learning. Regression models can help determine the _relationship_ between variables. This type of model can predict values such as length, temperature, or age, thus uncovering relationships between variables as it analyzes data points.
In this series of lessons, you'll discover the difference between Linear vs. Logistic Regression, and when you should use one or the other.
In this series of lessons, you'll discover the difference between linear vs. logistic regression, and when you should use one or the other.
In this group of lessons, you will get set up to begin machine learning tasks, including configuring Visual Studio code to manage notebooks, the common environment for data scientists. You will discover Scikit-learn, a library for machine learning, and you will build your first models, focusing on Regression models in this chapter.
> There are useful low-code tools that can help you learn about working with Regression models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
> There are useful low-code tools that can help you learn about working with regression models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
### Lessons
1. [Tools of the Trade](1-Tools/README.md)
2. [Managing Data](2-Data/README.md)
3. [Linear and Polynomial Regression](3-Linear/README.md)
4. [Logistic Regression](4-Logistic/README.md)
1. [Tools of the trade](1-Tools/README.md)
2. [Managing data](2-Data/README.md)
3. [Linear and polynomial regression](3-Linear/README.md)
4. [Logistic regression](4-Logistic/README.md)
---
### Credits
"ML with Regression" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper)
"ML with regression" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper)
## Regional topic: Delicious Asian and Indian Cuisines 🍜
In Asia and India, food traditions are extremely diverse, and very delicious! Let's look at data about regional cuisines to try to guess where they originated.
@ -8,18 +8,18 @@ In Asia and India, food traditions are extremely diverse, and very delicious! Le
## What you will learn
In this section, you will build on the skills you learned in Lesson 1 (Regression) to learn about other classifiers you can use that will help you learn about your data.
In this section, you will build on the skills you learned in the first part of this curriculum all about regressionn to learn about other classifiers you can use that will help you learn about your data.
> There are useful low-code tools that can help you learn about working with Classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
> There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
## Lessons
1. [Introduction to Classification](1-Introduction/README.md)
2. [More Classifiers](2-Classifiers-1/README.md)
3. [Yet Other Classifiers](3-Classifiers-2/README.md)
4. [Applied ML: Build a Web App](4-Applied/README.md)
1. [Introduction to classification](1-Introduction/README.md)
2. [More classifiers](2-Classifiers-1/README.md)
3. [Yet other classifiers](3-Classifiers-2/README.md)
4. [Applied ML: build a web app](4-Applied/README.md)
## Credits
"Getting Started with Classification" was written with ♥️ by [Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper)
"Getting started with classification" was written with ♥️ by [Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper)
The delicious cuisines dataset was sourced from [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)
## Regional topic: Clustering models for a Nigerian audience's musical taste 🎧
# Clustering models for machine learning
## Regional topic: clustering models for a Nigerian audience's musical taste 🎧
Nigeria's diverse audience has diverse musical tastes. Using data scraped from Spotify (inspired by [this article](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421), let's look at some music popular in Nigeria. This dataset includes data about various songs' 'danceability' score, 'acousticness', loudness, 'speechiness', popularity and energy. It will be interesting to discover patterns in this data!
@ -8,17 +8,17 @@ Nigeria's diverse audience has diverse musical tastes. Using data scraped from S
Photo by <ahref="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> on <ahref="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
In this series of lessons, you will discover new ways to analyze data using Clustering techniques. Clustering is particularly useful when your dataset lacks labels. If it does have labels, then Classification techniques such as those you learned in previous lessons are more useful. But in cases where you are looking to group unlabelled data, clustering is a great way to discover patterns.
In this series of lessons, you will discover new ways to analyze data using clustering techniques. Clustering is particularly useful when your dataset lacks labels. If it does have labels, then classification techniques such as those you learned in previous lessons might be more useful. But in cases where you are looking to group unlabelled data, clustering is a great way to discover patterns.
> There are useful low-code tools that can help you learn about working with Clustering models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
> There are useful low-code tools that can help you learn about working with clustering models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
## Lessons
1. [Introduction to Clustering](1-Visualize/README.md)
2. [K-Means Clustering](2-K-Means/README.md)
1. [Introduction to clustering](1-Visualize/README.md)
2. [K-Means clustering](2-K-Means/README.md)
## Credits
These lessons were written with 🎶 by [Jen Looper](https://www.twitter.com/jenlooper) with helpful reviews by [Rishit Dagli](https://rishit_dagli) and [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan).
The [Nigerian Songs](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) dataset was sourced from Kaggle as scraped from Spotify.
Useful K-Means examples that aided in creating this lesson include this [iris exploration](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), this [introductory notebook](https://www.kaggle.com/prashant111/k-means-clustering-with-python), this [hypothetical NGO example](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering) and
Useful K-Means examples that aided in creating this lesson include this [iris exploration](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), this [introductory notebook](https://www.kaggle.com/prashant111/k-means-clustering-with-python), and this [hypothetical NGO example](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering).
# Getting Started with natural language processing
# Getting started with natural language processing
## Regional topic: European literature and Romantic Hotels of Europe ❤️
## Regional topic: European languages and 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.
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.
> Photo by <ahref="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> on <ahref="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
@ -12,11 +12,10 @@ 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)
2. [Common NLP Tasks and Techniques](2-Tasks/README.md)
3. [Translation and Sentiment Analysis with Machine Learning](3-Translation-Sentiment/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
5. TBD
## 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](https://twitter.com/Howell_MSFT)
In these two lessons, you will be introduced to Time Series Forecasting, a somewhat lesser known area of Machine Learning that is nevertheless extremely valuable for industry and business applications, among other fields. While neural networks can be used to enhance the utility of these models, we will study them in the context of classical machine learning as models help predict future performance based on the past.
In these two lessons, you will be introduced to time series forecasting, a somewhat lesser known area of machine learning that is nevertheless extremely valuable for industry and business applications, among other fields. While neural networks can be used to enhance the utility of these models, we will study them in the context of classical machine learning as models help predict future performance based on the past.
Our regional focus is electrical usage in the world, an interesting dataset to learn about forecasting future power usage based on patterns of past load. You can see how this kind of forecasting can be extremely helpful in a business environment.
Photo by <ahref="https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Peddi Sai hrithik</a> of electrical towers on a road in Rajasthan on <ahref="https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
## Lessons
1. [Introduction to Time Series Forecasting](1-Introduction/README.md)
2. [Building ARIMA Time Series Models](2-ARIMA/README.md)
1. [Introduction to time series forecasting](1-Introduction/README.md)
2. [Building ARIMA time series models](2-ARIMA/README.md)
## Credits
"Time Series Forecasting" was written with ⚡️ by [Francesca Lazzeri](https://twitter.com/frlazzeri) and [Jen Looper](https://twitter.com/jenlooper)
"Introduction to time series forecasting" was written with ⚡️ by [Francesca Lazzeri](https://twitter.com/frlazzeri) and [Jen Looper](https://twitter.com/jenlooper)
[](https://www.youtube.com/watch?v=Fmi5zHg4QSM)
> 🎥 Click the image above to listen to Peter and the Wolf by Prokofiev
## Regional Topic: Peter and the Wolf (Russia)
## Regional topic: Peter and the Wolf (Russia)
[Peter and the Wolf](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) is a musical fairy tale written by a Russian composer [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). It is a story about young pioneer Peter, who bravely goes out of his house to the forest clearing to chase the wolf. In this section, we will train machine learning algorithms that will help Peter:
- **Explore** the surrounding area and build an optimal navigation map
- **Learn** how to use a skateboard and balance on it, in order to move around faster.
## Introduction to Reinforcement Learning
## Introduction to reinforcement learning
In previous sections, you have seen two example of machine learning problems:
In previous sections, you have seen two examples of machine learning problems:
* **Supervised**, where we had some datasets that show sample solutions to the problem we want to solve. [Classification](../4-Classification/README.md) and [Regression](../2-Regression/README.md) are supervised learning tasks.
* **Unsupervised**, in which we do not have training data. The main example of unsupervised learning is [Clustering](../5-Clustering/README.md).
* **Supervised**, where we have datasets that suggest sample solutions to the problem we want to solve. [Classification](../4-Classification/README.md) and [regression](../2-Regression/README.md) are supervised learning tasks.
* **Unsupervised**, in which we do not have labeled training data. The main example of unsupervised learning is [Clustering](../5-Clustering/README.md).
In this section, we will introduce you to a new type of learning problems, which do not require labeled training data. There are a several types of such problems:
In this section, we will introduce you to a new type of learning problems which do not require labeled training data. There are a several types of such problems:
* **[Semi-supervised learning](https://en.wikipedia.org/wiki/Semi-supervised_learning)**, where we have a lot of unlabeled data that can be used to pre-train the model.
* **[Reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning)**, in which the agent learns how to behave by performing a lot of experiments in some simulated environment.
* **[Semi-supervised learning](https://wikipedia.org/wiki/Semi-supervised_learning)**, where we have a lot of unlabeled data that can be used to pre-train the model.
* **[Reinforcement learning](https://wikipedia.org/wiki/Reinforcement_learning)**, in which an agent learns how to behave by performing experiments in some simulated environment.
Suppose, you want to teach computer to play a game, such as chess, or [Super Mario](https://en.wikipedia.org/wiki/Super_Mario). For computer to play a game, we need it to predict which move to make in each of the game states. While this may seem like a classification problem, it is not - because we do not have a dataset with states and corresponding actions. While we may have some data like that (existing chess matches, or recording of players playing Super Mario), it is likely not to cover sufficiently large number of possible states.
Suppose you want to teach computer to play a game, such as chess, or [Super Mario](https://wikipedia.org/wiki/Super_Mario). For the computer to play a game, we need it to predict which move to make in each of the game states. While this may seem like a classification problem, it is not - because we do not have a dataset with states and corresponding actions. While we may have some data like existing chess matches or recording of players playing Super Mario, it is likely that that data will not sufficiently cover a large enough number of possible states.
Instead of looking for existing game data, **Reinforcement Learning** (RL) is based on the idea of *making the computer play* many times, observing the result. Thus, to apply Reinforcement Learning, we need two things:
1. **An environment** and **a simulator**, which would allow us to play a game many times. This simulator would define all game rules, possible states and actions.
Instead of looking for existing game data, **Reinforcement Learning** (RL) is based on the idea of *making the computer play* many times and observing the result. Thus, to apply Reinforcement Learning, we need two things:
1. **An environment** and **a simulator** which allow us to play a game many times. This simulator would define all the game rules as well as possible states and actions.
2. **A reward function**, which would tell us how well we did during each move or game.
The main difference between supervised learning is that in RL we typically do not know whether we win or lose until we finish the game. Thus, we cannot say whether a certain move alone is good or now - we only receive reward at the end of the game. And our goal is to design such algorithms that will allow us to train a model under such uncertain conditions. We will learn about one RL algorithm called **Q-learning**.
The main difference between other types of machine learning and RL is that in RL we typically do not know whether we win or lose until we finish the game. Thus, we cannot say whether a certain move alone is good or not - we only receive a reward at the end of the game. And our goal is to design algorithms that will allow us to train a model under uncertain conditions. We will learn about one RL algorithm called **Q-learning**.
Jen Looper
3 years ago