Machine Learning Level1-self assessments1-grace yu

Technical Areas:

  • A deeper understanding of the usage of python(libraries)
  • A new perspective on machine learning(structure, approaches)
  • Generated new understanding of recommender system and learned about approaches to build a machine that learns
  • Get familiar with the different filtering approaches
  • New knowledge of python libraries

Tools:

  • Jupyter Notebook
  • Python
  • BeautifulSoup
  • Scrapy

Soft skills:

  • Learned new things from the webinars, such as usage of BeautifulSoup, the definition of HTML, Scraping and Crawling
  • Getting familiar with the remote intern pattern
  • Getting familiar with different tools (Github, Scrapy, BeautifulSoup, Jupyter Notebook)

3 achievements:

  • Review basic knowledge of python (I created several for loops, using a list to store values, searching certain data using a dictionary. Although I cannot successfully run my program every time, I successfully debug my program.)
  • First time more comprehensively acknowledge the concept of the recommender system (This is a very important step for me especially, since I had never learned about this topic, and a better understanding of the recommender system will benefit my further work with the team and the project. I am now having a better level of why a recommender system is created and how machine learning is associating with the recommender system in general. )
  • Tried to practice with the usage of BeautifulSoup and tried to create a similar program in python as the demo shown in the video. Successfully created a program and got the result of using frequency of the tags in the website (Quote to Scrap. However, I am still struggling to create a new program to scrap using BeautifulSoup and using the program to figure out the information I needed.)