Machine Learning - Level One Module One - Shreya

Concise Overview Of Things Learned

Technical

  1. Getting familiar with Google Colab and some basic Python syntax.
  2. Went over the typical machine learning project workflow.
  3. Learned the two main types of recommender systems (collaborative filtering or content based filtering) and where to use which one. Learning about different measures of similarity (cosine, dot product, and euclidean distance).
  4. Understood how NLP (Natural Language Processing) represents textual data as numeric values (one hot encoding, bag of words and word embeddings).
  5. Discovered data mining and how web scrapers or web crawlers (spiders) are used to collect and organize data.

Tools

  • Google Colaboratory
  • Beautiful Soup
  • Selenium
  • Scrapy
  • VS Code
  • Git and GitHub

Soft Skills

  • Ethical Web Crawling
  • Project Management (Agile framework → Scrum)

Achievement Highlights

  • Watched and took notes on all the STEMCasts, and some of the other resources provided by our mentor
  • Used BeautifulSoup and the requests library to scrape the content of a website
  • Downloaded Git and successfully pushed a change
  • Successfully created a web crawler with Scrapy and wrote the scraped content to a .csv file

Detailed Statement of Tasks Completed

  • Set up my workspace with all the required libraries
  • Became familiar with machine learning, and more specifically recommender systems
  • Understood NLP and how it is used in real applications
  • Learned about web scraping and web crawling and applied in my code
  • Was introduced to Git and practiced using it