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.
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)
@ -9,7 +9,7 @@ In this lesson and the following one, you will learn a bit about time series for
[](https://youtu.be/cBojo1hsHiI "Introduction to time series forecasting")
> 🎥 Click the image above for a video about time series forecasting
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?
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)
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)
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.
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.
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.
# 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.
Jen Looper
3 years ago