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@ -1,52 +0,0 @@
name: Azure Static Web Apps CI/CD
on:
push:
branches:
- main
pull_request:
types: [opened, synchronize, reopened, closed]
branches:
- main
jobs:
build_and_deploy_job:
if: github.event_name == 'push' || (github.event_name == 'pull_request' && github.event.action != 'closed')
runs-on: ubuntu-latest
name: Build and Deploy Job
permissions:
actions: read
contents: read
deployments: read
packages: none
pull-requests: write
security-events: write
steps:
- uses: actions/checkout@v2
with:
submodules: true
- name: Build And Deploy
id: builddeploy
uses: Azure/static-web-apps-deploy@v1
with:
azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_GRAY_SAND_07A10F403 }}
repo_token: ${{ secrets.GITHUB_TOKEN }} # Used for Github integrations (i.e. PR comments)
action: "upload"
###### Repository/Build Configurations - These values can be configured to match your app requirements. ######
# For more information regarding Static Web App workflow configurations, please visit: https://aka.ms/swaworkflowconfig
app_location: "/quiz-app" # App source code path
api_location: "" # Api source code path - optional
output_location: "dist" # Built app content directory - optional
###### End of Repository/Build Configurations ######
close_pull_request_job:
if: github.event_name == 'pull_request' && github.event.action == 'closed'
runs-on: ubuntu-latest
name: Close Pull Request Job
steps:
- name: Close Pull Request
id: closepullrequest
uses: Azure/static-web-apps-deploy@v1
with:
azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_GRAY_SAND_07A10F403 }}
action: "close"

@ -1,6 +1,6 @@
# Introduction to machine learning
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
---
@ -130,7 +130,7 @@ In the near future, understanding the basics of machine learning is going to be
Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com/), your understanding of the differences between AI, ML, deep learning, and data science. Add some ideas of problems that each of these techniques are good at solving.
# [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/)
# [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
---
# Review & Self Study

@ -3,7 +3,7 @@
![Summary of History of machine learning in a sketchnote](../../sketchnotes/ml-history.png)
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
---
@ -132,7 +132,7 @@ It remains to be seen what the future holds, but it is important to understand t
Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
---
## Review & Self Study

@ -3,7 +3,7 @@
![Summary of responsible AI in Machine Learning in a sketchnote](../../sketchnotes/ml-fairness.png)
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Introduction
@ -126,7 +126,8 @@ To prevent harms from being introduced in the first place, we should:
Think about real-life scenarios where a model's untrustworthiness is evident in model-building and usage. What else should we consider?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study
In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning.

@ -5,7 +5,7 @@ The process of building, using, and maintaining machine learning models and the
- Understand the processes underpinning machine learning at a high level.
- Explore base concepts such as 'models', 'predictions', and 'training data'.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/7/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
[![ML for beginners - Techniques of Machine Learning](https://img.youtube.com/vi/4NGM0U2ZSHU/0.jpg)](https://youtu.be/4NGM0U2ZSHU "ML for beginners - Techniques of Machine Learning")
@ -107,7 +107,7 @@ In these lessons, you will discover how to use these steps to prepare, build, te
Draw a flow chart reflecting the steps of a ML practitioner. Where do you see yourself right now in the process? Where do you predict you will find difficulty? What seems easy to you?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/8/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -4,7 +4,7 @@
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/9/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
> ### [This lesson is available in R!](./solution/R/lesson_1.html)
@ -213,7 +213,7 @@ Congratulations, you built your first linear regression model, created a predict
## 🚀Challenge
Plot a different variable from this dataset. Hint: edit this line: `X = X[:,2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/10/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -4,7 +4,7 @@
Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/11/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
> ### [This lesson is available in R!](./solution/R/lesson_2.html)
@ -200,7 +200,7 @@ To get charts to display useful data, you usually need to group the data somehow
Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/12/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -2,7 +2,7 @@
![Linear vs polynomial regression infographic](./images/linear-polynomial.png)
> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/13/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
> ### [This lesson is available in R!](./solution/R/lesson_3.html)
### Introduction
@ -356,7 +356,7 @@ This should give us the best determination coefficient of almost 97%, and MSE=2.
Test several different variables in this notebook to see how correlation corresponds to model accuracy.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -2,7 +2,7 @@
![Logistic vs. linear regression infographic](./images/linear-vs-logistic.png)
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
> ### [This lesson is available in R!](./solution/R/lesson_4.html)
@ -384,7 +384,7 @@ In future lessons on classifications, you will learn how to iterate to improve y
There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) for interesting datasets.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/16/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -11,7 +11,7 @@ We will continue our use of notebooks to clean data and train our model, but you
To do this, you need to build a web app using Flask.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/17/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Building an app
@ -334,7 +334,7 @@ In a professional setting, you can see how good communication is necessary betwe
Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/18/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -19,7 +19,7 @@ Remember:
Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
> ### [This lesson is available in R!](./solution/R/lesson_10.html)
@ -288,7 +288,7 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev
This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -4,7 +4,7 @@ In this lesson, you will use the dataset you saved from the last lesson full of
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://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
# Preparation
Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a _cleaned_cuisines.csv_ file exists in the root `/data` folder for these four lessons.
@ -231,7 +231,7 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of 'solver' to understand what goes on behind the scenes.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/22/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -2,7 +2,7 @@
In this second classification lesson, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one classifier over the other.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
### Prerequisite
@ -224,7 +224,7 @@ This method of Machine Learning "combines the predictions of several base estima
Each of these techniques has a large number of parameters that you can tweak. Research each one's default parameters and think about what tweaking these parameters would mean for the model's quality.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -8,7 +8,7 @@ One of the most useful practical uses of machine learning is building recommenda
> 🎥 Click the image above for a video: Jen Looper builds a web app using classified cuisine data
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/25/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
In this lesson you will learn:
@ -299,7 +299,7 @@ Congratulations, you have created a 'recommendation' web app with a few fields.
Your web app is very minimal, so continue to build it out using ingredients and their indexes from the [ingredient_indexes](../data/ingredient_indexes.csv) data. What flavor combinations work to create a given national dish?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/26/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -5,7 +5,9 @@ Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsup
[![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
> 🎥 Click the image above for a video. While you're studying machine learning with clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
### Introduction
[Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
@ -317,7 +319,7 @@ In general, for clustering, you can use scatterplots to show clusters of data, s
In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -1,6 +1,6 @@
# K-Means clustering
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
In this lesson, you will learn how to create clusters using Scikit-learn and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!
@ -234,7 +234,7 @@ Spend some time with this notebook, tweaking parameters. Can you improve the acc
Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -2,7 +2,7 @@
This lesson covers a brief history and important concepts of *natural language processing*, a subfield of *computational linguistics*.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/31/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Introduction
@ -149,7 +149,7 @@ Choose one of the "stop and consider" elements above and either try to implement
In the next lesson, you'll learn about a number of other approaches to parsing natural language and machine learning.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/32/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -2,7 +2,7 @@
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. These tasks, allows the programmer to derive the _meaning_ or _intent_ or only the _frequency_ of terms and words in a text.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/33/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
Let's discover common techniques used in processing text. Combined with machine learning, these techniques help you to analyse large amounts of text efficiently. Before applying ML to these tasks, however, let's understand the problems encountered by an NLP specialist.
@ -203,7 +203,7 @@ Implement the bot in the prior knowledge check and test it on a friend. Can it t
Take a task in the prior knowledge check and try to implement it. Test the bot on a friend. Can it trick them? Can you make your bot more 'believable?'
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/34/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -2,7 +2,7 @@
In the previous lessons you learned how to build a basic bot using `TextBlob`, a library that embeds ML behind-the-scenes to perform basic NLP tasks such as noun phrase extraction. Another important challenge in computational linguistics is accurate _translation_ of a sentence from one spoken or written language to another.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/35/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
Translation is a very hard problem compounded by the fact that there are thousands of languages and each can have very different grammar rules. One approach is to convert the formal grammar rules for one language, such as English, into a non-language dependent structure, and then translate it by converting back to another language. This approach means that you would take the following steps:
@ -176,7 +176,7 @@ Here is a sample [solution](https://github.com/microsoft/ML-For-Beginners/blob/m
Can you make Marvin even better by extracting other features from the user input?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/36/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -6,7 +6,7 @@ In this section you will use the techniques in the previous lessons to do some e
- how to calculate some new data based on the existing columns
- how to save the resulting dataset for use in the final challenge
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/37/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
### Introduction
@ -393,7 +393,7 @@ Now that you have explored the dataset, in the next lesson you will filter the d
This lesson demonstrates, as we saw in previous lessons, how critically important it is to understand your data and its foibles before performing operations on it. Text-based data, in particular, bears careful scrutiny. Dig through various text-heavy datasets and see if you can discover areas that could introduce bias or skewed sentiment into a model.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/38/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -1,7 +1,8 @@
# Sentiment analysis with hotel reviews
Now that you have explored the dataset in detail, it's time to filter the columns and then use NLP techniques on the dataset to gain new insights about the hotels.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/39/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
### Filtering & Sentiment Analysis Operations
@ -360,7 +361,7 @@ To review, the steps are:
When you started, you had a dataset with columns and data but not all of it could be verified or used. You've explored the data, filtered out what you don't need, converted tags into something useful, calculated your own averages, added some sentiment columns and hopefully, learned some interesting things about processing natural text.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/40/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Challenge

@ -10,7 +10,7 @@ In this lesson and the following one, you will learn a bit about time series for
> 🎥 Click the image above for a video about time series forecasting
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/41/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
It's a useful and interesting field with real value to business, given its direct application to problems of pricing, inventory, and supply chain issues. While deep learning techniques have started to be used to gain more insights to better predict future performance, time series forecasting remains a field greatly informed by classic ML techniques.
@ -174,7 +174,7 @@ In the next lesson, you will create an ARIMA model to create some forecasts.
Make a list of all the industries and areas of inquiry you can think of that would benefit from time series forecasting. Can you think of an application of these techniques in the arts? In Econometrics? Ecology? Retail? Industry? Finance? Where else?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/42/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -6,7 +6,7 @@ In the previous lesson, you learned a bit about time series forecasting and load
> 🎥 Click the image above for a video: A brief introduction to ARIMA models. The example is done in R, but the concepts are universal.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/43/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Introduction
@ -383,7 +383,7 @@ Check the accuracy of your model by testing its mean absolute percentage error (
Dig into the ways to test the accuracy of a Time Series Model. We touch on MAPE in this lesson, but are there other methods you could use? Research them and annotate them. A helpful document can be found [here](https://otexts.com/fpp2/accuracy.html)
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/44/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -2,7 +2,7 @@
In the previous lesson, you learned how to use ARIMA model to make time series predictions. Now you'll be looking at Support Vector Regressor model which is a regressor model used to predict continuous data.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/51/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Introduction
@ -367,7 +367,7 @@ MAPE: 2.0572089029888656 %
- Try to use different kernel functions for the model and analyze their performances on the dataset. A helpful document can be found [here](https://scikit-learn.org/stable/modules/svm.html#kernel-functions).
- Try using different values for `timesteps` for the model to look back to make prediction.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/52/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -11,7 +11,7 @@ By using reinforcement learning and a simulator (the game), you can learn how to
> 🎥 Click the image above to hear Dmitry discuss Reinforcement Learning
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/45/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Prerequisites and Setup
@ -314,7 +314,7 @@ The learnings can be summarized as:
Overall, it is important to remember that the success and quality of the learning process significantly depends on parameters, such as learning rate, learning rate decay, and discount factor. Those are often called **hyperparameters**, to distinguish them from **parameters**, which we optimize during training (for example, Q-Table coefficients). The process of finding the best hyperparameter values is called **hyperparameter optimization**, and it deserves a separate topic.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/46/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Assignment
[A More Realistic World](assignment.md)

@ -2,7 +2,7 @@
The problem we have been solving in the previous lesson might seem like a toy problem, not really applicable for real life scenarios. This is not the case, because many real world problems also share this scenario - including playing Chess or Go. They are similar, because we also have a board with given rules and a **discrete state**.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/47/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Introduction
@ -329,7 +329,7 @@ You should see something like this:
> **Task 4**: Here we were not selecting the best action on each step, but rather sampling with corresponding probability distribution. Would it make more sense to always select the best action, with the highest Q-Table value? This can be done by using `np.argmax` function to find out the action number corresponding to highers Q-Table value. Implement this strategy and see if it improves the balancing.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/48/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Assignment
[Train a Mountain Car](assignment.md)

@ -8,7 +8,7 @@ In this curriculum, you have learned many ways to prepare data for training and
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.
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/49/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## 💰 Finance
@ -136,7 +136,7 @@ The most effective marketing strategies target customers in different ways based
Identify another sector that benefits from some of the techniques you learned in this curriculum, and discover how it uses ML.
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/50/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study

@ -1,7 +1,7 @@
# Postscript: Model Debugging in Machine Learning using Responsible AI dashboard components
## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5/)
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Introduction
@ -143,7 +143,7 @@ To prevent statistical or data biases from being introduced in the first place,
Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider?
## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6/)
## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
## Review & Self Study
In this lesson, you have learned some of the practical tools of incorporating responsible AI in machine learning.

@ -90,14 +90,14 @@ By ensuring that the content aligns with projects, the process is made more enga
- optional sketchnote
- optional supplemental video
- video walkthrough (some lessons only)
- pre-lecture warmup quiz
- [pre-lecture warmup quiz](https://ff-quizzes.netlify.app/en/ml/)
- written lesson
- for project-based lessons, step-by-step guides on how to build the project
- knowledge checks
- a challenge
- supplemental reading
- assignment
- post-lecture quiz
- [post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/)
> **A note about languages**: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the `/solution` folder and look for R lessons. They include an .rmd extension that represents an **R Markdown** file which can be simply defined as an embedding of `code chunks` (of R or other languages) and a `YAML header` (that guides how to format outputs such as PDF) in a `Markdown document`. As such, it serves as an exemplary authoring framework for data science since it allows you to combine your code, its output, and your thoughts by allowing you to write them down in Markdown. Moreover, R Markdown documents can be rendered to output formats such as PDF, HTML, or Word.

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