Tutorials by students

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