Maxim Gorshkov - Machine Learning - Self Assessment

Week: 08/04/2020

Overview of Things Learned:

Technical Area:

  • Multi-label classification
  • Support Vector Machine; Linear/Gaussian kernels
  • Naive Bayes classifier

Tools:

  • NumPy
  • pandas
  • scikit-learn
  • NLTK (Natural Language Toolkit)

Soft Skills:

  • Research
  • Presentation
  • Communication
  • Teamwork

Achievement Highlights

  • Researched on SVM and Naive Bayes learning algorithms and how they apply to our classification problem
  • Implemented a draft version of the Python code responsible for text pre-processing, model training, and prediction
  • Shared my findings with the team and set our goals for the following week

Meetings attended

  • Weekly meetings on Tuesdays & Fridays
  • Project party on Monday

Goals for the Upcoming Week

  • Finish designing the Annotator (TagPredictor) class
  • Evaluate the performance of the learning algorithms/models under consideration
  • Fit the hyperparameters for the learning algorithms/models

Tasks Done

  • Decided on which pipeline to use for the project
  • Researched on learning algorithms for multi-label classification
  • Implemented prototypes of the Annotator and Classifier classes
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