Machine Learning - Level 1 Module 1 - Eeshan Walia

Technical Skills

  1. Learned about different Machine Learning models/libraries that can be used (Bag of Words, LSTM, BERT + BERT for classification problems.
  2. Learned how ML projects are run and the process behind designing ML models.
  3. Reviewed the Beautiful Soup and Selenium libraries in Python.
  4. Built my first very simple logistic regression model from a dataset that determined whether or not a tumor was malignant or benign.
  5. Explored data scraping.

Tools Used

  1. Google Colab + Jupyter Notebook (Gained familiarity)
  2. Kaggle (dataset on tumors)
  3. Beautiful Soup

Soft Skills

  1. Debugging - found and corrected errors that I encountered in my code.
  2. Resourcefulness - used various sources to find information to get an idea of the NLP/ML models and how I could model one myself.
  3. Organization - organized my workspace and kept track of the various tasks that I was undertaking.

Tasks

  1. Revisited both the Colab and Jupyter Notebooks: I have experience coding in Python in both notebooks and have done some basic AI in Colab, so I revisited both platforms just to make sure I was still able to use them properly.
  2. Explored the various uses of NLP and the general project flow for ML projects.
  3. Learned how to create my own Logistic Regression model and applied it to a specific set of data.
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