a) Technical Area:
Learned about what machine learning is used for and the basics of how it works.
Learned more in detail about (content-based) recommendation systems and natural language processing.
Learned about Web-Scraping through Python as well as Git.
Python - IDLE, Beautiful Soup, Selenium, “Requests” library,
Colab (Google ML tool)
c) Soft Skills
Watched a video on “Project Management Concepts and Tools”, learned about Scrum and Trello.
Learned more about how to gather information on Stack Overflow and other programming-help websites.
- Scraped on Quotes ToScrape and incorporated HTML parsing. Was able to store the file in a comma-separated format.
- Downloaded Jupyter Notebooks and created a simple “Hello World” notebook. Ultimately, I decided that the interface wasn’t for me and decided to stick with Colab but I learned a lot.
- I started expanding beyond this module a bit and learned about tensorflow and custom training. I trained a model to classify Iris flowers using a basic tensorflow tutorial. This wasn’t part of this module but it expanded my contextual understanding of natural language processing and machine learning in general.
Downloaded all the required materials and tools (Python 3.9, pip-installed beautifulsoup, selenium, requests), and made sure Github, Command Line, and Colab were set up properly.
Viewed Jay Alammar, Neptune-AI videos to get familiar with natural language processing.
Got a basic knowledge of BERT and PyTorch. I had some trouble with the latter at first, but after refreshing my knowledge of Python syntax and structure I was able to follow along with the example of the MNIST dataset.