*Overview of Things Learned*
- Went over the concepts of Machine Learning, including the concepts it was built on and the workflow required to create a machine learning project.
- Becoming familiar with the different models and approaches that machine learning can have on different cases and situations.
- Learned the basics of recommender systems and how they categorize data in order to determine similarity in between things using the different measures of similarity like cosine, euclidean distance, and dot product.
- Learned data mining and how to use and employ scraping and web crawlers in order to find and organize data that can then be fed into a program to produce some effect.
- Mathematical concepts are very important in machine learning, with an emphasis on linear algebra and vector math.
- Scrapy and BeautifulSoup for scraping and parsing through data
- VSCODE as an IDE
- Spacy for NLP
- Git and github for collaborative working
- Learning and understanding the guidelines for ethical web crawling
- Being able to use online discussion forums to understand and resolve problems that occur.
- Used and experimented with web scrapers and concepts to write a basic program that scrapes information off an NAQT, national association of quiz tournaments, website and sorts information based on player, school, and point per game.
- Experimented with git and how to use it more as I previously used Github desktop
- Created some more detailed and complex functions to be more familiar in python.
- Watched all of the STEMcast videos and also reviewed linear algebra and vector math as it was emphasized in the first video that it would be imperative in understanding machine learning.
**Detailed Statement of Tasks Completed**
- Became familiar with ideas and concepts behind machine learning and project management in a cooperative group setting.
- Created and improved on an old simple tournament bracketing algorithm for quiz bowl. I used the NAQT domain and information about how the URL is structured in order to prompt the user for input about a specific school and would return that school’s PPB, or point per bonus, and then sort them into the tournament accordingly. Originally, I encountered a big problem because of the way the HTML on the website was structured, where it assigned each school a number and the URL included that instead of the actual school name. I eventually resolved this by scraping first for the school’s number and then using that to navigate to the correct URL. I finished the preliminary parts of this, which is the majority of the scraping, but am still working on the sorting teams logic. I do not think I did the most efficient way in scraping but I will try using a webcrawler looking for stock data off webpages and see what I can do in terms of that. Please let me know if you have any suggestions or comments.
I have never written a self-assessment before and was wondering if I put everything in the correct location and formatted it correctly.
Also I just joined this on Thursday through PUSD and was not able to make the first meeting on Friday due to the timing. I am still slightly confused exactly how this will be structured and how people will be selected for the internship and if that will happen before or after we complete this pathway course. Thank you for your help!