Srishtiyadava - Machine Learning (Level 1) Pathway

Week 1

Overview of things I learned-

Technical area-

  1. I learned about the basics of machine learning and how it works with the help of the provided lectures and youtube videos.
  2. I learned about the basics of NLP.
  3. I learned about web scraping.

Tool-

Beautiful Soup, Jupyter Notebook, Git, GitHub, Trello, Discord.

Soft Skills-

  1. Learned how to communicate effectively with all the team members (got to know the members through several icebreakers).
  2. Learned time management skills (since the team members are from different time zones it was a little tricky to set a time for meetings but we worked it out).
  3. Learned teamwork for efficient workflow of the team.

Achievement Highlights-

  1. We picked a community to scrape.
  2. We set up a Trello board and selected a team name.
  3. I learned the basics of Beautiful Soup.

Tasks completed-

  1. I watched all the necessary tutorials to familiarize myself with the topic.
  2. I downloaded Jupyter Notebook and learned its basics.
  3. I downloaded the required Python Libraries and learned a little about it.

Goals for the upcoming week-

  1. Become more familiar with Beautiful Soup through tutorials.
  2. Practice web scraping.
  3. Start scraping the site.

Week 2

Overview of things I learned-

Technical Area-

  • Learned about the basic concepts of EDA.
  • Learned about how to scrape data from a forum.
  • Learned about saving files in CSV.

Tools-

Jupyter Notebook, Python, Beautiful Soup, Git/GitHub, Trello.

Soft Skills-

  • Learned Time Management.
  • Communication Skills- asked for help from the other team members if I couldn’t get through a problem.
  • I got better at researching solutions for errors.

Three achievement highlights-

  • Learned how to scrape data using Beautiful Soup.
  • Improved my knowledge of Python.
  • Joined the team GitHub repository.

Tasks completed-

  • Successfully scraped data with the help of Beautiful Soup.
  • Learned about HTML tags and application of EDA.
  • Analysed basic data and successfully organised it.

Goals for the upcoming week-

  • Save the data in CSV file.
  • Complete Module 2.
  • Push the code to the team GitHub.

Week 3

Overview of things I learned-

Technical Area-

  • Learned how to scrape a site using BeautifulSoup and Selenium.
  • Learned about saving files in CSV.
  • Learned about EDA.

Tools-

Jupyter Notebook, Python, BeautifulSoup, Selenium, Pandas, GitHub, Trello.

Soft Skills-

  • Learned Time Management.
  • Communication Skills- asked for help from the other team members when I couldn’t get through a problem.
  • I got better at researching solutions for errors.

Three achievement highlights-

  • Learned how to scrape data using BeautifulSoup and Selenium.
  • Improved my knowledge of Python.
  • I was able to solve some of the errors that occurred.

Tasks completed-

  • Successfully scraped data with the help of BeautifulSoup and Selenium.
  • Learned about HTML tags and application of EDA.

Goals for the upcoming week-

  • Prepare for Module 3.

Week 4

Overview of things I learned-

Technical Area-

  • Familiarized myself with the concept of classification models.
  • I familiarized myself about the basic recommender system with the help of tutorials.
  • Went through the resources provided on the STEM-away site.

Tools-

Jupyter Notebook, Colab, Python, BeautifulSoup, Selenium, Pandas, GitHub, Trello.

Soft Skills-

  • Learned Time Management.
  • Communication Skills- asked for help from the other team members when I couldn’t get through a problem.
  • I got better at researching solutions for errors.

Three achievement highlights-

  • I attended session 1 presentation to understand our future work process.
  • I was able to solve some of the errors that occurred.
  • Pushed my module 2 deliverables to the team GitHub repository.

Tasks completed-

  • Successfully completed EDA on the scraped data.
  • I familiarized myself with BERT.
  • I familiarized myself with what cosine similarity is.

Goals for the upcoming week-

  • Work on Module 3.

Week 5

Overview of things I learned-

Technical Area-

  • I familiarized myself with training and testing different classification models.
  • I read about the basic recommender system and watched tutorials.
  • Went through the resources provided on the STEM-away site.

Tools-

Jupyter Notebook, Colab, Pandas, nltk, Scikit-Learn, GitHub, Trello.

Soft Skills-

  • Learned Time Management.
  • Communication Skills- asked for help from the other team members when I couldn’t get through a problem.
  • I got better at researching solutions for errors.

Three achievement highlights-

  • Started training a Logistic Regression model.
  • I learnt about content based and collaborative filtering recommendation system.
  • I was able to solve some of the errors that occurred.

Tasks completed-

  • I gained more knowledge about BERT.
  • I learned about different classification models and their differences.

Goals for the upcoming week-

  • Complete Module 3.

Week 6

Overview of things I learned-

Technical Area-

  • I improved my knowledge on NLP.
  • I made a basic content based recommender system.
  • Went through the resources provided on the STEM-away site.

Tools-

Jupyter Notebook, Colab, Pandas, SimpleTransformers, Tokenizers, GitHub, Trello.

Soft Skills-

  • Learned Time Management.
  • Communication Skills- asked for help from the other team members when I couldn’t get through a problem.
  • I got better at researching solutions for errors.

Three achievement highlights-

  • I watched tutorials to build a basic recommender system which recommends top 10 similar posts.
  • I learnt about how to improve the accuracy level of a classifier using hyperparameter tuning.
  • I was able to solve some of the errors that occurred.

Tasks completed-

  • I trained a BERT classifier.
  • I trained several classifiers- Logistic Regression, SVM, Decision Tree, Naive-Bayes.
  • I learnt about the difference between several advanced classification models- BERT, RoBERTa, DistilBERT, XLNet.

Goals for the upcoming week-

  • Try to get my accuracy past 75% for my classifiers.