diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md index 1d7a03697c0d04d3d75fa16bde208f7adb28eebb..1137d79be330cb91cb0e227af4c21fbd49902584 100644 --- a/1-Introduction/1-intro-to-ML/README.md +++ b/1-Introduction/1-intro-to-ML/README.md @@ -2,7 +2,7 @@ [](https://youtu.be/lTd9RSxS9ZE "ML, AI, Deep Learning - What's the difference?") -python path: https://docs.microsoft.com/en-us/learn/paths/python-language/ +python path: https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa > 馃帴 Click the image above for a video discussing the difference between Machine Learning, AI, and Deep Learning. ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/) @@ -45,6 +45,7 @@ conclusion ## Review & Self Study +include this path: https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/ to warm up to these topics ## Assignment [Get up and running](assignment.md) diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md index 88b2b81f2a57886e929fc7793e0fc85d03c1a380..6b14057593bef694c5284400b7f66fba7006d180 100644 --- a/1-Introduction/3-fairness/README.md +++ b/1-Introduction/3-fairness/README.md @@ -21,7 +21,7 @@ In this lesson, you will: As a prerequisite, please take the "Responsible AI Principles" Learn Path and watch the video below on the topic: -Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa) +Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa) [](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI") > 馃帴 Click the image above for a video: Microsoft's Approach to Responsible AI @@ -156,7 +156,7 @@ The tool helps you to assesses how a model's predictions affect different groups - Try some [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks). -- Learn [how to enable fairness assessments](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) of machine learning models in Azure Machine Learning. +- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) of machine learning models in Azure Machine Learning. - Check out these [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning. ## 馃殌 Challenge @@ -190,7 +190,7 @@ Explore the Fairlearn toolkit Read about Azure Machine Learning's tools to ensure fairness -- [Azure Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) +- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) ## Assignment diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 0e43b50257216cd28fcd13e9dc6251ad26f6f30f..7164fa63ffb097bcb4e1f32089fb93c669e7068b 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -20,7 +20,6 @@ Having worked with the pumpkin data, we are now familiar enough with it to reali For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway. > 馃巸 Fun fact, we sometimes call white pumpkins 'ghost' pumpkins. They aren't very easy to carve, so they aren't as popular as the orange ones but they are cool looking! - ## About Logistic Regression Logistic Regression differs from Linear Regression, which you learned about previously, in a few important ways. diff --git a/2-Regression/README.md b/2-Regression/README.md index e5ffa300d4756d15b536c92aed5ae2e2b56f6a57..621a6f4233515dcd6398ce4be5c9948c65579349 100644 --- a/2-Regression/README.md +++ b/2-Regression/README.md @@ -10,6 +10,8 @@ In North America, pumpkins are often carved into scary faces for Halloween. Let' In this section, 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) + ### Lessons 1. [Tools of the Trade](1-Tools/README.md) diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md index 8996b3e4872973153e2f2d16d761b9e213aabc50..146d33d7726648c7673b7f6cff528fb3ba1cc595 100644 --- a/4-Classification/2-Classifiers-1/README.md +++ b/4-Classification/2-Classifiers-1/README.md @@ -1,8 +1,9 @@ # Recipe Classifiers 1 In this lesson, you will use the dataset you saved from the last lesson full of balanced, clean data all about recipes. You will use this dataset with a variety of classifiers to predict a given national cuisine based on a group of ingredients. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks. + ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/) -# Preparatory steps to start this lesson +# Preparation Assuming you completed Lesson 1, make sure that a `cleaned_cuisines.csv` file exists in the root `/data` folder for these four lessons. @@ -74,7 +75,24 @@ Now you are ready to train your model! Now that your data is clean and ready for training, you have to decide which algorithm to use for the job. -TODO: discuss the types +Scikit-Learn groups Classification under Supervised Learning, and in that category you will find many ways to classify. [The variety](https://scikit-learn.org/stable/supervised_learning.html) is quite bewildering at first sight. The following methods all include classification techniques: + +- Linear Models +- Support Vector Machines +- Stochastic Gradient Descent +- Nearest Neighbors +- Gaussian Processes +- Decision Trees +- Ensemble methods (voting Classifier) +- Multiclass and multioutput algorithms (multiclass and multilabel classification, multiclass-multioutput classification) + +You can also use [neural networks to classify](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification), but that is outside the scope of this lesson. + +So, which classifier should you choose? Often, running through several and looking for a good result is a way to test. Scikit-Learn offers a [side-by-side comparison](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) on a created dataset, comparing KNeighbors, SVC two ways, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB and QuadraticDiscrinationAnalysis, showing the results visualized: + + + +> AutoML solves this problem neatly by running these comparisons in the cloud, allowing you to choose the best algorithm for your data. Try it [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa) 鉁� Todo: knowledge check diff --git a/4-Classification/2-Classifiers-1/images/comparison.png b/4-Classification/2-Classifiers-1/images/comparison.png new file mode 100644 index 0000000000000000000000000000000000000000..b5526545b432db6a2c62cb3430b7262cc0733291 Binary files /dev/null and b/4-Classification/2-Classifiers-1/images/comparison.png differ diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md index ed70e4563987a58761ca7162f127905ae635770f..4b8f8624a9406e4263235e0192158d7590be336b 100644 --- a/4-Classification/3-Classifiers-2/README.md +++ b/4-Classification/3-Classifiers-2/README.md @@ -1,9 +1,6 @@ # Recipe Classifiers 2 -Add a sketchnote if possible/appropriate - - - +In this second Classification lesson, you will explore more ways to classify data, and the ramifications for choosing one over the other. ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/) Describe what we will learn diff --git a/4-Classification/README.md b/4-Classification/README.md index dbf5843630175258c81f780bd419f689e3e5359c..2d79618100e01028c0c4a3468ab018ca8e020147 100644 --- a/4-Classification/README.md +++ b/4-Classification/README.md @@ -9,6 +9,9 @@ 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. + +> 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) diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md index 718ced99097e2f231fe8148d81dcb06b3f4699fe..8ed046b8c922a08a0ecea56f0956f0be9c02e753 100644 --- a/5-Clustering/1-Visualize/README.md +++ b/5-Clustering/1-Visualize/README.md @@ -26,7 +26,7 @@ Alternately, you could use it for grouping search results - by shopping links, i 鉁� Once your data is organized in clusters, you assign it a cluster Id, and this technique can be useful when preserving a dataset's privacy; you can instead refer to a data point by its cluster id, rather than by more revealing identifiable data. Can you think of other reasons why you'd refer to a cluster Id rather than other elements of the cluster to identify it? -Deepen your understanding of Clustering techniques in this [Learn module](https://docs.microsoft.com/en-us/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa) +Deepen your understanding of Clustering techniques in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa) ## Getting started with clustering [Scikit-Learn offers a large array](https://scikit-learn.org/stable/modules/clustering.html) of methods to perform clustering. The type you choose will depend on your use case. According to the documentation, each method has various benefits. Here is a simplified table of the methods supported by Scikit-Learn and their appropriate use cases: diff --git a/5-Clustering/README.md b/5-Clustering/README.md index 67424f25ec0f798f2101d412c3c6c976ecef2a3e..3fc1b307254cbbf45d9d6bebe306727c0930a079 100644 --- a/5-Clustering/README.md +++ b/5-Clustering/README.md @@ -9,6 +9,8 @@ Photo by <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_ 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. + +> 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) diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md index 7524a07fc5c60211fde06c11d39611a109053741..c21d8a03140795a961beb94ae8c77399c86eb259 100644 --- a/6-NLP/3-Translation-Sentiment/README.md +++ b/6-NLP/3-Translation-Sentiment/README.md @@ -142,7 +142,7 @@ Can you make Marvin even better by extracting other features from the user input ## Review & Self Study -There are many ways to extract sentiment from text. Think of the business applications that might make use of this technique. Think about how it can go awry. Read more about sophisticated enterprise-ready systems that analyze sentiment such as [Azure Text Analysis](https://docs.microsoft.com/en-us/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa). Test some of the Pride and Prejudice sentences above and see if it can detect nuance. +There are many ways to extract sentiment from text. Think of the business applications that might make use of this technique. Think about how it can go awry. Read more about sophisticated enterprise-ready systems that analyze sentiment such as [Azure Text Analysis](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa). Test some of the Pride and Prejudice sentences above and see if it can detect nuance. ## Assignment diff --git a/SECURITY.md b/SECURITY.md index f7b89984f0fb5dd204028bc525e19eefc0859f4f..c5082ddfd57262051fff918073409b79ff5acfbc 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -4,7 +4,7 @@ Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). -If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below. +If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/previous-versions/tn-archive/cc751383(v=technet.10)?WT.mc_id=academic-15963-cxa), please report it to us as described below. ## Reporting Security Issues