links to Learn added

1-Introduction/1-intro-to-ML/README.md

@@ -2,7 +2,7 @@
 
 [![ML, AI, Deep Learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](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
 
 > 🎥 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)

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)
 
 [![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
 > 🎥 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. 
 ## 🚀 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
 

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.
 
 > 🎃 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.

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)

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: 
+
+![comparison of classifiers](images/comparison.png)
+
+> 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)
 
 ✅ Todo: knowledge check
 

4-Classification/3-Classifiers-2/README.md

@@ -1,9 +1,6 @@
 # Recipe Classifiers 2
 
-Add a sketchnote if possible/appropriate
-
-![Embed a video here if available](video-url)
-
+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

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)

5-Clustering/1-Visualize/README.md

@@ -26,7 +26,7 @@ Alternately, you could use it for grouping search results - by shopping links, i
 
 ✅ 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:

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)

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 
 

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