One of the assignments in the course was to write a tutorial on almost any ML/DS-related topic. Here is the result. Slack nicks of the authors are given with @’s.

Spring 2019 session#

  1. “Overview of imbalance-learn package” – Kaggle Kernel by @pdepdepde

  2. “Extracting cricket scorecards of batsman and bowlers from ESPN cricinfo” – Kaggle Kernel by @Rajasekhar Battula

  3. “LDA, PCA and topic modelling” – Kaggle Kernel by @Shivam Panwar

  4. “Visualization with Bokeh” – Kaggle Kernel by @Sita

  5. “Autoencoders and t-SNE” – Kaggle Kernel by @goku

  6. “Implementing Gradient Descent” – Kaggle Kernel by @Ana Hristian

  7. “ML interpretability” – Kaggle Kernel by @Christophe Rigon

  8. “ML In Chemistry Research: RDKit & mol2vec” – Kaggle Kernel by @Vlad Kisin

  9. “Bayesian methods of hyperparameter optimization” – Kaggle Kernel by @clair

  10. “Python Data Pre-Processing – Handy Tips” – Kaggle Kernel by @Shravan Kumar Koninti

  11. “Categorical Feature Encoding” – Kaggle Kernel by @Wayde Herman

  12. “Unsupervised Learning: Clustering” – Kaggle Kernel by @Maximgolovatchev

  13. “Collaborative filtering with PySpark” – Kaggle Kernel by @vchulski

  14. “AutoML capabilities of H2O library” – Kaggle Kernel by @Dmitry Burdeiny

  15. “Factorization machine implemented in PyTorch” – Kaggle Kernel by @GL

  16. “CatBoost overview” – Kaggle Kernel by @MITribunskiy

  17. “Hyperopt” – Kaggle Kernel by @fanvacoolt

Fall 2018 session#

  1. “Plotly for interactive plots” by Alexander Kovalev (@velavok) – nbviewer

  2. “Basic semi-supervised learning models” by Gleb Levitski (@altprof) – nbviewer

  3. “Yet another ensemble learning helper” by Aleksandr Korotkov (@krotix) – nbviewer

  4. “Imputing missing data with fancyimpute” by Archit Rungta (@Archit Rungta) – nbviewer

  5. “Risk management with Python” by Andrey Varkentin (@varan) – nbviewer

  6. “Insights of Monty Hall paradox with Plotly” by Denis Mironov (@dmironov) – nbviewer

  7. “Epidemics on networks with NetworkX and EoN” by Ilya Syrovatskiy (@bokomaru) – nbviewer

  8. “LDA (Linear Discriminant Analysis) and LDA vs PCA” by Shivam Panwar (@Shivam Panwar) – nbviewer

  9. “A little more info about NumPy” by Ksenia Terekhova (@Kseniia) – nbviewer

  10. “Forget about GridSearch – how to tune hyperparameters using Hyperopt” by Ilya Larchenko (@ilya_l) – nbviewer

  11. “Merging DataFrames with pandas” by Max Palko (@odpalko) – nbviewer

  12. “A Tutorial On Understanding ([Rr]ege)(x|xp|xes|xps|xen)” by Aditya Soni (@ecdrid) – nbviewer

  13. “Leaderboard probing” by Nikolai Timonin (@timoninn) – nbviewer

  14. “Mlxtend.SFS: an easy way to select features” by Anton Gilmanov (@wicker) – nbviewer

  15. “Bring your plots to life with Matplotlib animations” by Kyriacos Kyriacou (@kyr) – nbviewer

  16. “Handle different dataset with dask and trying a little dask ML” by Irina Knyazeva (@Iknyazeva) – nbviewer

  17. “Feature engineering is all you need” by Georgy Surin (@formemorte) – nbviewer

  18. “Latent Dirichlet Allocation” by Valentin Kovalev (@Valentin) – nbviewer

  19. “Handling categorical variables” by Danila Perepechin (@Danila) – nbviewer

  20. “Introduction to Network Analysis with NetworkX” by Georgy Lazarev (@jorgy) – nbviewer

  21. “Webscraping an online retailer assortment” by Maxim Keremet (@maximkeremet) – nbviewer

  22. “Some details on Matplotlib” by Ivan Pisarev (@pisarev_i) – nbviewer

  23. “Statistical hypothesis testing in Python” by Kirill Panin (@Kirill Panin) – nbviewer

  24. “Nested cross-validation” by Tatyana Kudasova (@kudasova) – nbviewer

  25. “Intuitive explanation of Expectation Maximization” by Neeraj Agrawal (@MagnIeeT) – nbviewer

  26. “Scraping websites with help of Selenium” by Vadim Voskresenskii (@Vadimvoskresenskiy) – nbviewer

  27. “Constructing simple Chatbot using spaCy” by Ilya Kalininskii (@Kiavip) – nbviewer

  28. “LSTM (Long Short Term Memory) Networks for predicting Time Series” by Sergei Bulaev (@Ser-serege) – nbviewer

  29. “How to predict catastrophic events?” by Joris Fournell (@Jorisfournell) – nbviewer

  30. “Anomaly Detection: Isolation Forest” by Alexander Nichiporenko (@AlexNich) – nbviewer

  31. “Something else about ensemble learning” by Dmitry Korgun (@tbb) – nbviewer

  32. “KERAS: easy way to construct the Neural Networks” by Natalia Domozhirova (@ndomozhirova) – nbviewer

  33. “Deploying your Machine Learning Model” by Maxim Klyuchnikov (@jabberwock) – nbviewer

  34. “Virtual environment for learning ML” by Mikhail Korshchikov (@Mikhailsergeevi4) – nbviewer

  35. “Self organizing map” by Nikita Simonov (@simanjan) – nbviewer

  36. “Useful Google Colab snippets” by Denic Cera (@Dene) – nbviewer

  37. “Can we create our own text vectorizer?” by Alexander Ashikhmin (@alex.ash) – nbviewer

  38. “Collecting information for machine learning purposes. Parsing and Grabbing” by Alexander Laskorunskiy (@a_lasko) – nbviewer

More tutorials and individual projects (in Russian) are listed in the Wiki section of the course repo.