diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md index dba067d0..fc7b916f 100644 --- a/6-NLP/4-Hotel-Reviews-1/README.md +++ b/6-NLP/4-Hotel-Reviews-1/README.md @@ -2,6 +2,8 @@ In this section you will use the techniques in the previous lessons to do some exploratory data analysis of a large dataset. Once you have a good understanding of the usefulness of the various columns, you will learn how to remove the unneeded columns, calculate some new data based on the existing columns, and save the resulting dataset for use in the final challenge. +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/37/) + ### Introduction So far you've learned about how text data is quite unlike numerical types of data. If it's text that was written or spoken by a human, if can be analysed to find patterns and frequencies, sentiment and meaning. This final lesson takes you into a real data set with a real challenge. This lesson is a lot of code and analysis of a data set, it is quite dense but very amenable to experimentation in your favourite IDE or Notebook. @@ -501,7 +503,7 @@ df.to_csv(r'Hotel_Reviews_Filtered.csv', index = False) -## [Post-lecture quiz](tbd) +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/38/) ## Review & Self Study diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md index e69de29b..f1d8f9d8 100644 --- a/6-NLP/5-Hotel-Reviews-2/README.md +++ b/6-NLP/5-Hotel-Reviews-2/README.md @@ -0,0 +1,11 @@ +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/39/) + +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/40/) + +## Review & Self Study + +Explore SMOTE's API. What use cases is it best used for? What problems does it solve? + +## Assignment + +[Explore classification methods](assignment.md) diff --git a/7-TimeSeries/1-Introduction/README.md b/7-TimeSeries/1-Introduction/README.md index c09a1b71..bff3b390 100644 --- a/7-TimeSeries/1-Introduction/README.md +++ b/7-TimeSeries/1-Introduction/README.md @@ -9,7 +9,7 @@ In this lesson and the following one, you will learn a bit about time series for [![Introduction to time series forecasting](https://img.youtube.com/vi/cBojo1hsHiI/0.jpg)](https://youtu.be/cBojo1hsHiI "Introduction to time series forecasting") > 🎥 Click the image above for a video about time series forecasting -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/39/) +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/41/) 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 in the prediction of future performance, time series forecasting remains a field greatly informed by classic ML techniques. @@ -162,7 +162,7 @@ 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/40/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/42/) ## Review & Self Study Although we won't cover them here, neural networks are sometimes used to enhance classic methods of time series forecasting. Read more about them [in this article](https://medium.com/microsoftazure/neural-networks-for-forecasting-financial-and-economic-time-series-6aca370ff412) diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md index 8ec821be..20f03377 100644 --- a/7-TimeSeries/2-ARIMA/README.md +++ b/7-TimeSeries/2-ARIMA/README.md @@ -5,7 +5,7 @@ In the previous lesson, you learned a bit about time series forecasting and load [![Introduction to ARIMA](https://img.youtube.com/vi/IUSk-YDau10/0.jpg)](https://youtu.be/IUSk-YDau10 "Introduction to ARIMA") > 🎥 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/41/) +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/43/) In this lesson, you will discover a specific way to build models with [ARIMA: *A*uto*R*egressive *I*ntegrated *M*oving *A*verage](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average). ARIMA models are particularly suited to fit data that shows [non-stationarity](https://wikipedia.org/wiki/Stationary_process). @@ -369,7 +369,7 @@ plt.show() 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/42/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/44/) ## Review & Self Study This lesson touches on only the basics of Time Series Forecasting with ARIMA. Take some time to deepen your knowledge by digging into [this repository](https://microsoft.github.io/forecasting/) and its various model types to learn other ways to build Time Series models. diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md index d856c0f1..b6fe6b2e 100644 --- a/8-Reinforcement/1-QLearning/README.md +++ b/8-Reinforcement/1-QLearning/README.md @@ -3,7 +3,7 @@ [![Intro to Reinforcement Learning](https://img.youtube.com/vi/lDq_en8RNOo/0.jpg)](https://www.youtube.com/watch?v=lDq_en8RNOo) > 🎥 Click the image above to hear Dmitry discuss Reinforcement Learning -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/43/) +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/45/) In this lesson, we will explore the world of **[Peter and the Wolf](https://en.wikipedia.org/wiki/Peter_and_the_Wolf)**, inspired by a musical fairy tale by a Russian composer, [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). We will use **Reinforcement Learning** to let Peter explore his environment, collect tasty apples and avoid meeting the wolf. @@ -291,6 +291,6 @@ What we also observe on this graph is that at some point, the length increased a 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/44/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/46/) ## Assignment [A More Realistic World](assignment.md) diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md index c0b03688..ae53c392 100644 --- a/8-Reinforcement/2-Gym/README.md +++ b/8-Reinforcement/2-Gym/README.md @@ -10,6 +10,8 @@ We will use a simplified version of balancing known as a **CartPole** problem. I a cartpole +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/47/) + ## Prerequisites In this lesson, we will be using a library called **OpenAI Gym** to simulate different **environments**. You can run this lesson's code locally (eg. from Visual Studio Code), in which case the simulation will open in a new window. When running the code online, you may need to make some tweaks to the code, as described [here](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7). @@ -305,8 +307,7 @@ 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](link-to-quiz-app) - +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/48/) ## Assignment: [Train a Mountain Car](assignment.md) ## Conclusion diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md index 39a17cb2..fe6a1794 100644 --- a/9-Real-World/1-Applications/README.md +++ b/9-Real-World/1-Applications/README.md @@ -1,10 +1,10 @@ -# Machine learning in the real world +# Machine learning in the real world - a postscript 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. -## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/47/) +## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/49/) ## 💰 Finance @@ -131,7 +131,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/ 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://jolly-sea-0a877260f.azurestaticapps.net/quiz/48/) +## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/50/) ## Review & Self Study diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json index f69135e5..e992a0c6 100644 --- a/quiz-app/src/assets/translations/en.json +++ b/quiz-app/src/assets/translations/en.json @@ -2144,6 +2144,112 @@ }, { "id": 39, + "title": "NLP 5: Pre-Lecture Quiz", + "quiz": [ + { + "questionText": "q1", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q2", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "true" + }, + { + "answerText": "b", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q3", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 40, + "title": "NLP 5: Post-Lecture Quiz", + "quiz": [ + { + "questionText": "q1", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q2", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "true" + }, + { + "answerText": "b", + "isCorrect": "false" + } + ] + }, + { + "questionText": "q3", + "answerOptions": [ + { + "answerText": "a", + "isCorrect": "false" + }, + { + "answerText": "b", + "isCorrect": "true" + }, + { + "answerText": "c", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 41, "title": "Intro to Time Series: Pre-Lecture Quiz", "quiz": [ { @@ -2200,7 +2306,7 @@ ] }, { - "id": 40, + "id": 42, "title": "Intro to Time Series: Post-Lecture Quiz", "quiz": [ { @@ -2257,7 +2363,7 @@ ] }, { - "id": 41, + "id": 43, "title": "Time Series ARIMA: Pre-Lecture Quiz", "quiz": [ { @@ -2314,7 +2420,7 @@ ] }, { - "id": 42, + "id": 44, "title": "Time Series ARIMA: Post-Lecture Quiz", "quiz": [ { @@ -2371,7 +2477,7 @@ ] }, { - "id": 43, + "id": 45, "title": "Reinforcement 1: Pre-Lecture Quiz", "quiz": [ { @@ -2424,7 +2530,7 @@ ] }, { - "id": 44, + "id": 46, "title": "Reinforcement 1: Post-Lecture Quiz", "quiz": [ { @@ -2477,7 +2583,7 @@ ] }, { - "id": 45, + "id": 47, "title": "Reinforcement 2: Pre-Lecture Quiz", "quiz": [ { @@ -2530,7 +2636,7 @@ ] }, { - "id": 46, + "id": 48, "title": "Reinforcement 2: Post-Lecture Quiz", "quiz": [ { @@ -2583,7 +2689,7 @@ ] }, { - "id": 47, + "id": 49, "title": "Real World Applications: Pre-Lecture Quiz", "quiz": [ { @@ -2640,7 +2746,7 @@ ] }, { - "id": 48, + "id": 50, "title": "Real World Applications: Post-Lecture Quiz", "quiz": [ {