Current course assignments, to be account for in rating:

  1. Exploratory Data Analysis of Olympic games with Pandas, nbviewer. Deadline: October 14, 21:59 UTC+2
  2. Exploratory Data Analysis of US flights, nbviewer. Deadline: October 21, 21:59 UTC+2
  3. Decision trees. nbviewer. Deadline: October 28, 21:59 UTC+2. Optional: implementing a decision tree algorithm, nvbiewer (no webforms and credits, the same deadline)
  4. Logisitic regression. nbviewer. Deadline: November 4, 21:59 UTC+2
  5. Random Forest and Logistic Regression in credit scoring and movie reviews classification. nbviewer. Deadline: November 11, 21:59 UTC+2
  6. Beating baselines in “How good is your Medium article?”. nbviewer. Deadline: November 18, 21:59 UTC+2
  7. PCA and clustering. nbviewer. Deadline: November 25, 21:59 UTC+2

Demo assignments, just for practice (completing these has no effect on rating):

  1. Exploratory data analysis with Pandas, nbviewer, Kaggle Kernel
  2. Analyzing cardiovascular disease data, nbviewer, Kaggle Kernel
  3. Decision trees with a toy task and the UCI Adult dataset, nbviewer, Kaggle Kernel
  4. Linear Regression as an optimization problem, nbviewer, Kaggle Kernel
  5. Logistic Regression and Random Forest in the credit scoring problem, nbviewer, Kaggle Kernel
  6. Exploring OLS, Lasso and Random Forest in a regression task, nbviewer, Kaggle Kernel
  7. Unsupervised learning, nbviewer, Kaggle Kernel
  8. Implementing online regressor, nbviewer, Kaggle Kernel
  9. Time series analysis, nbviewer, Kaggle Kernel
  10. Gradient boosting and flight delays, nbviewer, Kaggle Kernel