Ekamgrewal - Machine Learning (Level 1) Pathway

Technical Area:

  • Learned more about overview concepts of Machine learning and some machine learning algorithms by watching the lecture videos
  • Learned about fundamental ideas about NLP and some networks like Vanilla Neural Networks, Recurrent Neural Networks (RNNS), and Long Short-Term Memory Networks (LSTM)
  • Learned about web scraping and data mining


  • PyTorch
  • Spacy
  • BeautifulSoup
  • TensorFlow

Soft Skills:

  • Got a good overview of ML better from Kunal Singh’s video
  • Understood application of ML better via sorting IMDB movie example.
  • Got an idea about crawling, scraping, and NLP.
  • Learn Python better

Achievements and tasks:

  • Learned about concepts of Machine Learning and web scraping
  • Got all of the required material installed (Python libraries, VS Code)
  • Built virtual environment
  • Learned a lot of new stuff about git and git-hub

Module 2


  • Scraping data using BeautifulSoup
  • Cleaning the data
  • Familiarized me with data preprocessing and data cleaning
  • Learned how the requests library allows users to directly interact with HTML websites
  • Used requests, Pandas, and BeautifulSoup to scrape data and then convert it into a CSV file


  • Google Colab
  • YouTube
  • Python Libraries (BeautifulSoup, Pandas, Requests)
  • Discourse (CarTalk Forum)

Soft Skills:

  • Data preprocessing
  • Debugging


  • Successfully scraped the data from the CarTalk forum (on discourse)
  • Convert data into CSV using the pandas library
  • Understood how to use BeautifulSoup to scrape data


  • Learned different concepts from Youtube videos and the resources on EDA, etc.
  • Created a new Google Colab
  • Scraped data from: CarTalk forum
  • Converted data into a CSV file using pandas library

Module 3

Technical Area

  • Used Selenium Webdriver to scrape websites I found
  • Used Pandas to play around with and clean/organize data
  • Learned about basic recommender systems


  • CSV
  • Selenium
  • PyCharm
  • NumPy
  • Pandas

Soft Skills

  • Learned how to better Google information to help me in my learning process
  • Found videos and forums that helped improve my understanding of machine learning concepts and the tools I was using

Achievement Highlights

  • Downloaded and learned about Selenium’s purpose/uses, watched a few tutorials
  • Created a basic recommender system, using cosine similarity
  • Used different embeddings with machine learning models to see classification results of data

Tasks and Hurdles

  • Made a simple recommender system using cosine similarity, it took me some time but I had it recommend similar posts through similar titles.
  • I tried training and testing different classification models, and I struggled in getting accurate results.