Your Mission (Continued)
~1 Hour 20 Minutes
Build on our work from class to formalize your play from the last level. Once you've merged the data from Dewey, Cheetham, and Howe, created some potentially useful features, and turned everything into numbers, upload a csv of your data to GitHub with the name dewey-cheetham-howe.csv. See
https://[your username].github.io/ctl/dewey-cheetham-howe.csv
FWIW, preparing instructional material is an exercise in compression, and it's not lossless. The hope is that you now have a very high-level sense of how things work. For example, I glossed over how the activation function behaves with regard to the word2vec "hidden"/projection layer. Spoiler: it's not a sigmoid! Actually, there is no activation function. We just pass on the weights. That being said, I didn't explain weights very deeply. So again, as it says in the title—oversimplification. ;)
Optional Media.
If you want to learn more about some of the topics discussed in the video above, and you have some free time, you might enjoy the following.
Readings
~ 1 Hour and 10 Minutes
Knowledge Base
Everyone comes to this adventure with a different background. So this section is designed to be a menu of sorts. If you already know a topic well, you can skip the relevant material. Just answer the questions below, and section(s) will disappear accordingly. That being said, if a section doesn't disappear, you should do it. Any time you save skipping a topic, however, should be spent working on your final project or reading ahead in either Weapons of Math Destruction or How Not to Be Wrong. FYI, we will be reading all of Weapons of Math Destruction and all but parts III and V of How Not to Be Wrong.
All of that being said, let's see if we can pare things down.
Are you proficient with QnA Markup?
You've gained roughly 30 minutes by dropping a video introduction to QnA Markup. FWIW, you're going to be asked to create an interview in QnA Markup at the end of this Level. If you find yourself with questions, change this answer to unhide the QnA introduction.
Do you have a good text editor? I'm not asking about a word processor, there's a difference.
You've gained roughly 10 minutes by dropping a section on installing a text editor.
Do you have a GitHub account, and do you know how to use it?
You've gained roughly 20 minutes by dropping a GitHub exercise that walks you through creating a reop and making a pull request et al.
Training Your Algos: Linear Regressions
7-14 Minutes.
Note: none of your missions will ask you to perform a linear regression. So don't worry too much about the details discussed above. Given how much we've talked about regressions, I figured we should build at least one together as a class.
We are working with the notebook file training.ipynb (pre-loaded for those of you using Pythonanywhere).
Optional: If you want to learn more about some of the topics discussed in the video above, and you have some free time, you might enjoy the following: Guess the Correlation (a video game where you guess the R-squared).
Training Your Algos: Binary Classifiers
13-26 Minutes.
Again, we are working with the notebook named training.ipynb (pre-loaded for those of you using Pythonanywhere).
Optional: If you want to learn more about some of the topics discussed in the video above, and you have some free time, you might enjoy the following.
Your Mission (Continued)
~1 Hour
Take the data you prepared above (i.e., https://[your username].github.io/ctl/dewey-cheetham-howe.csv) and use it to train the classifiers found in training.ipynb under the Classifiers Section. Remember I've only loaded libraries for the Python 3.5 kernel. So if you make a new notebook, be sure it's using 3.5.
Stretch Goal: Add a new classification algo from scikit-learn to your notebook. Take a screen shot of the evaluation screen for this algo, and upload it to GitHub with the name new_algo. E.g.,
https://[your username].github.io/ctl/new_algo.png
Note you should follow the pattern of the algos shown above. That is, you can just copy one of the example cells, swap out the first two lines and edit variable names accordingly. You might find some inspiration here.
Your Final Project
Enrolled students will be presenting on their final project in one week. Take whatever time you have left to work on your project, even if it's just planning or skills acquisition (e.g., working through the optional docassemble training from level 3). See The Final Project Rubric.
Self-Reflection and Logging Your Work
~20 min
As we do at the end of every level, we ask that you take a few minutes to reflect on how things are going. I've also included a set of reading questions to queue things up for our synchronous discussion. Your answers will be shared with me and it will let me know that I can look for any project work you may have posted. That being said, you've almost completed Level 7. Tell me how it's going by completing the form linked below.
Synchronous Meet Up, AKA our Class Time
1 Hour and 30 Minutes | October 13, 2020 @ 4pm Eastern
If you're an enrolled student, we'll be meeting at this link on TUESDAY October 13th at 4pm via Zoom. If you don't have the password, and you are a registered student, DM me on Slack, and I can give you the password. If you're not an enrolled student, I'm afraid you can't join us.
We will use this time to: (1) troubleshoot any issues folks might have had working through the your mission; and (2) discuss the readings.
† Time estimates are just that—estimates. The assumptions used to calculate reading time are as follows: 48 pages is assumed to take roughly an hour to read. When working with non paginated texts, it is assumed that a page is roughly equal to 250 words. Videos assume both 2X and 1X viewing. Estimates for coding are based on past experience. Each level should include about 6 hours and 40 min of work.