Tutorials#
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#
“Overview of imbalance-learn package” – Kaggle Kernel by @pdepdepde
“Extracting cricket scorecards of batsman and bowlers from ESPN cricinfo” – Kaggle Kernel by @Rajasekhar Battula
“LDA, PCA and topic modelling” – Kaggle Kernel by @Shivam Panwar
“Visualization with Bokeh” – Kaggle Kernel by @Sita
“Autoencoders and t-SNE” – Kaggle Kernel by @goku
“Implementing Gradient Descent” – Kaggle Kernel by @Ana Hristian
“ML interpretability” – Kaggle Kernel by @Christophe Rigon
“ML In Chemistry Research: RDKit & mol2vec” – Kaggle Kernel by @Vlad Kisin
“Bayesian methods of hyperparameter optimization” – Kaggle Kernel by @clair
“Python Data Pre-Processing – Handy Tips” – Kaggle Kernel by @Shravan Kumar Koninti
“Categorical Feature Encoding” – Kaggle Kernel by @Wayde Herman
“Unsupervised Learning: Clustering” – Kaggle Kernel by @Maximgolovatchev
“Collaborative filtering with PySpark” – Kaggle Kernel by @vchulski
“AutoML capabilities of H2O library” – Kaggle Kernel by @Dmitry Burdeiny
“Factorization machine implemented in PyTorch” – Kaggle Kernel by @GL
“CatBoost overview” – Kaggle Kernel by @MITribunskiy
“Hyperopt” – Kaggle Kernel by @fanvacoolt
Fall 2018 session#
“Plotly for interactive plots” by Alexander Kovalev (@velavok) – nbviewer
“Basic semi-supervised learning models” by Gleb Levitski (@altprof) – nbviewer
“Yet another ensemble learning helper” by Aleksandr Korotkov (@krotix) – nbviewer
“Imputing missing data with fancyimpute” by Archit Rungta (@Archit Rungta) – nbviewer
“Risk management with Python” by Andrey Varkentin (@varan) – nbviewer
“Insights of Monty Hall paradox with Plotly” by Denis Mironov (@dmironov) – nbviewer
“Epidemics on networks with NetworkX and EoN” by Ilya Syrovatskiy (@bokomaru) – nbviewer
“LDA (Linear Discriminant Analysis) and LDA vs PCA” by Shivam Panwar (@Shivam Panwar) – nbviewer
“A little more info about NumPy” by Ksenia Terekhova (@Kseniia) – nbviewer
“Forget about GridSearch – how to tune hyperparameters using Hyperopt” by Ilya Larchenko (@ilya_l) – nbviewer
“Merging DataFrames with pandas” by Max Palko (@odpalko) – nbviewer
“A Tutorial On Understanding ([Rr]ege)(x|xp|xes|xps|xen)” by Aditya Soni (@ecdrid) – nbviewer
“Leaderboard probing” by Nikolai Timonin (@timoninn) – nbviewer
“Mlxtend.SFS: an easy way to select features” by Anton Gilmanov (@wicker) – nbviewer
“Bring your plots to life with Matplotlib animations” by Kyriacos Kyriacou (@kyr) – nbviewer
“Handle different dataset with dask and trying a little dask ML” by Irina Knyazeva (@Iknyazeva) – nbviewer
“Feature engineering is all you need” by Georgy Surin (@formemorte) – nbviewer
“Latent Dirichlet Allocation” by Valentin Kovalev (@Valentin) – nbviewer
“Handling categorical variables” by Danila Perepechin (@Danila) – nbviewer
“Introduction to Network Analysis with NetworkX” by Georgy Lazarev (@jorgy) – nbviewer
“Webscraping an online retailer assortment” by Maxim Keremet (@maximkeremet) – nbviewer
“Some details on Matplotlib” by Ivan Pisarev (@pisarev_i) – nbviewer
“Statistical hypothesis testing in Python” by Kirill Panin (@Kirill Panin) – nbviewer
“Nested cross-validation” by Tatyana Kudasova (@kudasova) – nbviewer
“Intuitive explanation of Expectation Maximization” by Neeraj Agrawal (@MagnIeeT) – nbviewer
“Scraping websites with help of Selenium” by Vadim Voskresenskii (@Vadimvoskresenskiy) – nbviewer
“Constructing simple Chatbot using spaCy” by Ilya Kalininskii (@Kiavip) – nbviewer
“LSTM (Long Short Term Memory) Networks for predicting Time Series” by Sergei Bulaev (@Ser-serege) – nbviewer
“How to predict catastrophic events?” by Joris Fournell (@Jorisfournell) – nbviewer
“Anomaly Detection: Isolation Forest” by Alexander Nichiporenko (@AlexNich) – nbviewer
“Something else about ensemble learning” by Dmitry Korgun (@tbb) – nbviewer
“KERAS: easy way to construct the Neural Networks” by Natalia Domozhirova (@ndomozhirova) – nbviewer
“Deploying your Machine Learning Model” by Maxim Klyuchnikov (@jabberwock) – nbviewer
“Virtual environment for learning ML” by Mikhail Korshchikov (@Mikhailsergeevi4) – nbviewer
“Self organizing map” by Nikita Simonov (@simanjan) – nbviewer
“Useful Google Colab snippets” by Denic Cera (@Dene) – nbviewer
“Can we create our own text vectorizer?” by Alexander Ashikhmin (@alex.ash) – nbviewer
“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.