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| Beazley, David M._Jones, Brian Kenneth - Python cookbook (2014, O'Reilly Media) - libgen.lc.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Beazley%2C%20David%20M._Jones%2C%20Brian%20Kenneth%20-%20Python%20cookbook%20(2014%2C%20O'Reilly%20Media)%20-%20libgen.lc.pdf |
| Beazley, David M._Jones, Brian Kenneth - Python cookbook (2014, O'Reilly Media) - libgen.lc.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Beazley%2C%20David%20M._Jones%2C%20Brian%20Kenneth%20-%20Python%20cookbook%20(2014%2C%20O'Reilly%20Media)%20-%20libgen.lc.pdf |
| Buss, Ian_ George, Lars_ Kunigk, Jan_ Wilkinson, Paul - Architecting modern data platforms _ a guide to enterprise Hadoop at scale (2019, O’Reilly Media, Inc) - libgen.lc.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Buss%2C%20Ian_%20George%2C%20Lars_%20Kunigk%2C%20Jan_%20Wilkinson%2C%20Paul%20-%20Architecting%20modern%20data%20platforms%20_%20a%20guide%20to%20enterprise%20Hadoop%20at%20scale%20(2019%2C%20O%E2%80%99Reilly%20Media%2C%20Inc)%20-%20libgen.lc.pdf |
| Buss, Ian_ George, Lars_ Kunigk, Jan_ Wilkinson, Paul - Architecting modern data platforms _ a guide to enterprise Hadoop at scale (2019, O’Reilly Media, Inc) - libgen.lc.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Buss%2C%20Ian_%20George%2C%20Lars_%20Kunigk%2C%20Jan_%20Wilkinson%2C%20Paul%20-%20Architecting%20modern%20data%20platforms%20_%20a%20guide%20to%20enterprise%20Hadoop%20at%20scale%20(2019%2C%20O%E2%80%99Reilly%20Media%2C%20Inc)%20-%20libgen.lc.pdf |
| Chris Albon - Machine Learning with Python Cookbook_ Practical Solutions from Preprocessing to Deep Learning (2018, O’Reilly Media) - libgen.lc.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Chris%20Albon%20-%20Machine%20Learning%20with%20Python%20Cookbook_%20Practical%20Solutions%20from%20Preprocessing%20to%20Deep%20Learning%20(2018%2C%20O%E2%80%99Reilly%20Media)%20-%20libgen.lc.pdf |
| Chris Albon - Machine Learning with Python Cookbook_ Practical Solutions from Preprocessing to Deep Learning (2018, O’Reilly Media) - libgen.lc.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Chris%20Albon%20-%20Machine%20Learning%20with%20Python%20Cookbook_%20Practical%20Solutions%20from%20Preprocessing%20to%20Deep%20Learning%20(2018%2C%20O%E2%80%99Reilly%20Media)%20-%20libgen.lc.pdf |
| DS_quick_sample_tutorial.ipynb | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/DS_quick_sample_tutorial.ipynb |
| DS_quick_sample_tutorial.ipynb | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/DS_quick_sample_tutorial.ipynb |
| DataScienceMLOverview.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/DataScienceMLOverview.pdf |
| DataScienceMLOverview.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/DataScienceMLOverview.pdf |
| Hemant Jain - Problem Solving in Data Structures & Algorithms Using Python_ Programming Interview Guide (2016, Createspace Independent Publishing Platform) - libgen.li.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Hemant%20Jain%20-%20Problem%20Solving%20in%20Data%20Structures%20%26%20Algorithms%20Using%20Python_%20Programming%20Interview%20Guide%20(2016%2C%20Createspace%20Independent%20Publishing%20Platform)%20-%20libgen.li.pdf |
| Hemant Jain - Problem Solving in Data Structures & Algorithms Using Python_ Programming Interview Guide (2016, Createspace Independent Publishing Platform) - libgen.li.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Hemant%20Jain%20-%20Problem%20Solving%20in%20Data%20Structures%20%26%20Algorithms%20Using%20Python_%20Programming%20Interview%20Guide%20(2016%2C%20Createspace%20Independent%20Publishing%20Platform)%20-%20libgen.li.pdf |
| Joe Reis_ Matt Housley - Fundamentals of Data Engineering (2022, O'Reilly Media, Inc.) - libgen.li.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Joe%20Reis_%20Matt%20Housley%20-%20Fundamentals%20of%20Data%20Engineering%20(2022%2C%20O'Reilly%20Media%2C%20Inc.)%20-%20libgen.li.pdf |
| Joe Reis_ Matt Housley - Fundamentals of Data Engineering (2022, O'Reilly Media, Inc.) - libgen.li.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Joe%20Reis_%20Matt%20Housley%20-%20Fundamentals%20of%20Data%20Engineering%20(2022%2C%20O'Reilly%20Media%2C%20Inc.)%20-%20libgen.li.pdf |
| Narasimha Karumanchi - Data Structure and Algorithmic Thinking with Python_ Data Structure and Algorithmic Puzzles (2020, CareerMonk Publications) - libgen.li.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Narasimha%20Karumanchi%20-%20Data%20Structure%20and%20Algorithmic%20Thinking%20with%20Python_%20Data%20Structure%20and%20Algorithmic%20Puzzles%20(2020%2C%20CareerMonk%20Publications)%20-%20libgen.li.pdf |
| Narasimha Karumanchi - Data Structure and Algorithmic Thinking with Python_ Data Structure and Algorithmic Puzzles (2020, CareerMonk Publications) - libgen.li.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/Narasimha%20Karumanchi%20-%20Data%20Structure%20and%20Algorithmic%20Thinking%20with%20Python_%20Data%20Structure%20and%20Algorithmic%20Puzzles%20(2020%2C%20CareerMonk%20Publications)%20-%20libgen.li.pdf |
| README.md | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/README.md |
| README.md | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/README.md |
| [Undergraduate Topics in Computer Science] Kent D. Lee, Steve Hubbard - Data Structures and Algorithms with Python (2015, Springer) - libgen.lc.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/%5BUndergraduate%20Topics%20in%20Computer%20Science%5D%20Kent%20D.%20Lee%2C%20Steve%20Hubbard%20-%20Data%20Structures%20and%20Algorithms%20with%20Python%20(2015%2C%20Springer)%20-%20libgen.lc.pdf |
| [Undergraduate Topics in Computer Science] Kent D. Lee, Steve Hubbard - Data Structures and Algorithms with Python (2015, Springer) - libgen.lc.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/%5BUndergraduate%20Topics%20in%20Computer%20Science%5D%20Kent%20D.%20Lee%2C%20Steve%20Hubbard%20-%20Data%20Structures%20and%20Algorithms%20with%20Python%20(2015%2C%20Springer)%20-%20libgen.lc.pdf |
| data_architecture.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/data_architecture.pdf |
| data_architecture.pdf | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/data_architecture.pdf |
| loan_train_data.csv | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/loan_train_data.csv |
| loan_train_data.csv | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/loan_train_data.csv |
| pngdslogo.png | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/pngdslogo.png |
| pngdslogo.png | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/pngdslogo.png |
| README | https://github.com/indmitDS/Machine-Learning-Tutorial |
| https://github.com/indmitDS/Machine-Learning-Tutorial#ml-exercise |
| https://github.com/indmitDS/Machine-Learning-Tutorial#data-science-with-python |
| https://github.com/indmitDS/Machine-Learning-Tutorial#books |
| Introduction to Statistical Learning | https://web.stanford.edu/~hastie/ISLRv2_website.pdf |
| Hands-On Machine Learning with Scikit-Learn and TensorFlow | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/github.com/ageron/handson-ml2 |
| The Hundred Page Machine learning Book | http://ema.cri-info.cm/wp-content/uploads/2019/07/2019BurkovTheHundred-pageMachineLearning.pdf/ |
| Introduction to ML with Python - Guid | https://files.isec.pt/DOCUMENTOS/SERVICOS/BIBLIO/INFORMA%C3%87%C3%95ES%20ADICIONAIS/Introction-to-machine_Muller.pdf/ |
| Applied Machine Learning | https://link.springer.com/book/10.1007/978-3-030-18114-7/ |
| Algorithms to Know for ML | https://github.com/PacktPublishing/40-Algorithms-Every-Programmer-Should-Know/ |
| Transact SQL Book | http://justpain.com/eBooks/Databases/SQL/Transact-SQL/The%20Guru's%20Guide%20To%20Transact-SQL.pdf/ |
| https://github.com/indmitDS/Machine-Learning-Tutorial#core |
| pandas | https://pandas.pydata.org/ |
| numpy | https://www.numpy.org/ |
| scikit-learn | https://scikit-learn.org/stable/ |
| matplotlib | https://matplotlib.org/ |
| seaborn | https://seaborn.pydata.org/ |
| pandas_summary | https://github.com/mouradmourafiq/pandas-summary |
| pandas_profiling | https://github.com/pandas-profiling/pandas-profiling |
| sklearn_pandas | https://github.com/scikit-learn-contrib/sklearn-pandas |
| missingno | https://github.com/ResidentMario/missingno |
| rainbow-csv | https://marketplace.visualstudio.com/items?itemName=mechatroner.rainbow-csv |
| https://github.com/indmitDS/Machine-Learning-Tutorial#environment-and-jupyter |
| Jupyter Desktop Installation | https://analyticsindiamag.com/jupyter-labs-desktop-app-what-is-it-do-we-need-it/ |
| General Jupyter Tricks | https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/ |
| link | https://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/ |
| blog post | https://www.blog.pythonlibrary.org/2018/10/17/jupyter-notebook-debugging/ |
| video | https://www.youtube.com/watch?v=Z0ssNAbe81M&t=1h44m15s |
| cheatsheet | https://nblock.org/2011/11/15/pdb-cheatsheet/ |
| cookiecutter-data-science | https://github.com/drivendata/cookiecutter-data-science |
| nteract | https://nteract.io/ |
| papermill | https://github.com/nteract/papermill |
| tutorial | https://pbpython.com/papermil-rclone-report-1.html |
| nbdime | https://github.com/jupyter/nbdime |
| ReviewNB | https://www.reviewnb.com/ |
| RISE | https://github.com/damianavila/RISE |
| qgrid | https://github.com/quantopian/qgrid |
| pivottablejs | https://github.com/nicolaskruchten/jupyter_pivottablejs |
| itables | https://github.com/mwouts/itables |
| jupyter-datatables | https://github.com/CermakM/jupyter-datatables |
| debugger | https://blog.jupyter.org/a-visual-debugger-for-jupyter-914e61716559 |
| nbcommands | https://github.com/vinayak-mehta/nbcommands |
| handcalcs | https://github.com/connorferster/handcalcs |
| https://github.com/indmitDS/Machine-Learning-Tutorial#pandas-tricks-alternatives-and-additions |
| Pandas Tricks | https://towardsdatascience.com/5-lesser-known-pandas-tricks-e8ab1dd21431 |
| Using df.pipe() (video) | https://www.youtube.com/watch?v=yXGCKqo5cEY |
| pandasvault | https://github.com/firmai/pandasvault |
| modin | https://github.com/modin-project/modin |
| vaex | https://github.com/vaexio/vaex |
| pandarallel | https://github.com/nalepae/pandarallel |
| xarray | https://github.com/pydata/xarray/ |
| swifter | https://github.com/jmcarpenter2/swifter |
| pandas_flavor | https://github.com/Zsailer/pandas_flavor |
| pandas-log | https://github.com/eyaltrabelsi/pandas-log |
| pandapy | https://github.com/firmai/pandapy |
| https://github.com/indmitDS/Machine-Learning-Tutorial#helpful |
| drawdata | https://github.com/koaning/drawdata |
| website | https://drawdata.xyz/ |
| tqdm | https://github.com/tqdm/tqdm |
| pandas apply() | https://stackoverflow.com/a/34365537/1820480 |
| icecream | https://github.com/gruns/icecream |
| loguru | https://github.com/Delgan/loguru |
| pyprojroot | https://github.com/chendaniely/pyprojroot |
| intake | https://github.com/intake/intake |
| talk | https://www.youtube.com/watch?v=s7Ww5-vD2Os&t=33m40s |
| https://github.com/indmitDS/Machine-Learning-Tutorial#extraction |
| textract | https://github.com/deanmalmgren/textract |
| camelot | https://github.com/socialcopsdev/camelot |
| https://github.com/indmitDS/Machine-Learning-Tutorial#big-data |
| spark | https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html#work-with-dataframes |
| cheatsheet | https://gist.github.com/crawles/b47e23da8218af0b9bd9d47f5242d189 |
| tutorial | https://github.com/ericxiao251/spark-syntax |
| sparkit-learn | https://github.com/lensacom/sparkit-learn |
| spark-deep-learning | https://github.com/databricks/spark-deep-learning |
| koalas | https://github.com/databricks/koalas |
| dask | https://github.com/dask/dask |
| dask-ml | http://ml.dask.org/ |
| resources | https://matthewrocklin.com/blog//work/2018/07/17/dask-dev |
| talk1 | https://www.youtube.com/watch?v=ccfsbuqsjgI |
| talk2 | https://www.youtube.com/watch?v=RA_2qdipVng |
| notebooks | https://github.com/dask/dask-ec2/tree/master/notebooks |
| videos | https://www.youtube.com/user/mdrocklin |
| dask-gateway | https://github.com/jcrist/dask-gateway |
| turicreate | https://github.com/apple/turicreate |
| h2o | https://github.com/h2oai/h2o-3 |
| datatable | https://github.com/h2oai/datatable |
| cuDF | https://github.com/rapidsai/cudf |
| Intro | https://www.youtube.com/watch?v=6XzS5XcpicM&t=2m50s |
| ray | https://github.com/ray-project/ray/ |
| mars | https://github.com/mars-project/mars |
| bottleneck | https://github.com/kwgoodman/bottleneck |
| bolz | https://github.com/Blosc/bcolz |
| cupy | https://github.com/cupy/cupy |
| petastorm | https://github.com/uber/petastorm |
| zarr | https://github.com/zarr-developers/zarr-python |
| https://github.com/indmitDS/Machine-Learning-Tutorial#command-line-tools-csv |
| ni | https://github.com/spencertipping/ni |
| xsv | https://github.com/BurntSushi/xsv |
| csvkit | https://csvkit.readthedocs.io/en/1.0.3/ |
| csvsort | https://pypi.org/project/csvsort/ |
| tsv-utils | https://github.com/eBay/tsv-utils |
| cheat | https://github.com/cheat/cheat |
| https://github.com/indmitDS/Machine-Learning-Tutorial#classical-statistics |
| https://github.com/indmitDS/Machine-Learning-Tutorial#statistical-tests-and-packages |
| Verifying the Assumptions of Linear Models | https://github.com/erykml/medium_articles/blob/master/Statistics/linear_regression_assumptions.ipynb |
| Mediation and Moderation Intro | https://ademos.people.uic.edu/Chapter14.html |
| statsmodels | https://www.statsmodels.org/stable/index.html |
| pingouin | https://github.com/raphaelvallat/pingouin |
| Pairwise correlation between columns of pandas DataFrame | https://pingouin-stats.org/generated/pingouin.pairwise_corr.html |
| scipy.stats | https://docs.scipy.org/doc/scipy/reference/stats.html#statistical-tests |
| scikit-posthocs | https://github.com/maximtrp/scikit-posthocs |
| 1 | https://pingouin-stats.org/generated/pingouin.plot_blandaltman.html |
| 2 | http://www.statsmodels.org/dev/generated/statsmodels.graphics.agreement.mean_diff_plot.html |
| ANOVA | https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html |
| One-way | https://pythonfordatascience.org/anova-python/ |
| Two-way | https://pythonfordatascience.org/anova-2-way-n-way/ |
| Type 1,2,3 explained | https://mcfromnz.wordpress.com/2011/03/02/anova-type-iiiiii-ss-explained/ |
| https://github.com/indmitDS/Machine-Learning-Tutorial#interim-analyses--sequential-analysis--stopping |
| Squential Analysis | https://en.wikipedia.org/wiki/Sequential_analysis |
| Treatment Effects Monitoring | https://online.stat.psu.edu/stat509/node/75/ |
| sequential | https://cran.r-project.org/web/packages/Sequential/Sequential.pdf |
| confseq | https://github.com/gostevehoward/confseq |
| https://github.com/indmitDS/Machine-Learning-Tutorial#visualizations |
| Null Hypothesis Significance Testing (NHST) and Sample Size Calculation | https://rpsychologist.com/d3/NHST/ |
| Correlation | https://rpsychologist.com/d3/correlation/ |
| Cohen's d | https://rpsychologist.com/d3/cohend/ |
| Confidence Interval | https://rpsychologist.com/d3/CI/ |
| Equivalence, non-inferiority and superiority testing | https://rpsychologist.com/d3/equivalence/ |
| Bayesian two-sample t test | https://rpsychologist.com/d3/bayes/ |
| Distribution of p-values when comparing two groups | https://rpsychologist.com/d3/pdist/ |
| Understanding the t-distribution and its normal approximation | https://rpsychologist.com/d3/tdist/ |
| https://github.com/indmitDS/Machine-Learning-Tutorial#talks |
| Inverse Propensity Weighting | https://www.youtube.com/watch?v=SUq0shKLPPs |
| Dealing with Selection Bias By Propensity Based Feature Selection | https://www.youtube.com/watch?reload=9&v=3ZWCKr0vDtc |
| https://github.com/indmitDS/Machine-Learning-Tutorial#texts |
| Greenland - Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4877414/ |
| Lindeløv - Common statistical tests are linear models | https://lindeloev.github.io/tests-as-linear/ |
| Chatruc - The Central Limit Theorem and its misuse | https://lambdaclass.com/data_etudes/central_limit_theorem_misuse/ |
| Al-Saleh - Properties of the Standard Deviation that are Rarely Mentioned in Classrooms | http://www.stat.tugraz.at/AJS/ausg093/093Al-Saleh.pdf |
| Wainer - The Most Dangerous Equation | http://www-stat.wharton.upenn.edu/~hwainer/Readings/Most%20Dangerous%20eqn.pdf |
| Gigerenzer - The Bias Bias in Behavioral Economics | https://www.nowpublishers.com/article/Details/RBE-0092 |
| Cook - Estimating the chances of something that hasn’t happened yet | https://www.johndcook.com/blog/2010/03/30/statistical-rule-of-three/ |
| https://github.com/indmitDS/Machine-Learning-Tutorial#epidemiology |
| R Epidemics Consortium | https://www.repidemicsconsortium.org/projects/ |
| Github | https://github.com/reconhub |
| incidence2 | https://github.com/reconhub/incidence2 |
| EpiEstim | https://github.com/mrc-ide/EpiEstim |
| paper | https://academic.oup.com/aje/article/178/9/1505/89262 |
| researchpy | https://github.com/researchpy/researchpy |
| zEpid | https://github.com/pzivich/zEpid |
| Tutorial | https://github.com/pzivich/Python-for-Epidemiologists |
| https://github.com/indmitDS/Machine-Learning-Tutorial#exploration-and-cleaning |
| Checklist | https://github.com/r0f1/ml_checklist |
| cleanlab | https://github.com/cgnorthcutt/cleanlab |
| pandasgui | https://github.com/adamerose/pandasgui |
| janitor | https://pyjanitor.readthedocs.io/ |
| impyute | https://github.com/eltonlaw/impyute |
| fancyimpute | https://github.com/iskandr/fancyimpute |
| imbalanced-learn | https://github.com/scikit-learn-contrib/imbalanced-learn |
| tspreprocess | https://github.com/MaxBenChrist/tspreprocess |
| Kaggler | https://github.com/jeongyoonlee/Kaggler |
| pyupset | https://github.com/ImSoErgodic/py-upset |
| pyemd | https://github.com/wmayner/pyemd |
| OpenCV implementation | https://docs.opencv.org/3.4/d6/dc7/group__imgproc__hist.html |
| POT implementation | https://pythonot.github.io/auto_examples/plot_OT_2D_samples.html |
| littleballoffur | https://github.com/benedekrozemberczki/littleballoffur |
| https://github.com/indmitDS/Machine-Learning-Tutorial#train--test-split |
| iterative-stratification | https://github.com/trent-b/iterative-stratification |
| https://github.com/indmitDS/Machine-Learning-Tutorial#feature-engineering |
| Talk | https://www.youtube.com/watch?v=68ABAU_V8qI |
| sklearn | https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html |
| examples | https://github.com/jem1031/pandas-pipelines-custom-transformers |
| pdpipe | https://github.com/shaypal5/pdpipe |
| scikit-lego | https://github.com/koaning/scikit-lego |
| skoot | https://github.com/tgsmith61591/skoot |
| categorical-encoding | https://github.com/scikit-learn-contrib/categorical-encoding |
| vtreat (R package) | https://cran.r-project.org/web/packages/vtreat/vignettes/vtreat.html |
| dirty_cat | https://github.com/dirty-cat/dirty_cat |
| patsy | https://github.com/pydata/patsy/ |
| mlxtend | https://rasbt.github.io/mlxtend/user_guide/feature_extraction/LinearDiscriminantAnalysis/ |
| featuretools | https://github.com/Featuretools/featuretools |
| example | https://github.com/WillKoehrsen/automated-feature-engineering/blob/master/walk_through/Automated_Feature_Engineering.ipynb |
| tsfresh | https://github.com/blue-yonder/tsfresh |
| pypeln | https://github.com/cgarciae/pypeln |
| feature_engine | https://github.com/solegalli/feature_engine |
| https://github.com/indmitDS/Machine-Learning-Tutorial#feature-engineering-images |
| skimage | https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops |
| mahotas | https://github.com/luispedro/mahotas |
| pyradiomics | https://github.com/AIM-Harvard/pyradiomics |
| pyefd | https://github.com/hbldh/pyefd |
| https://github.com/indmitDS/Machine-Learning-Tutorial#feature-selection |
| Talk | https://www.youtube.com/watch?v=JsArBz46_3s |
| Repo | https://github.com/Yimeng-Zhang/feature-engineering-and-feature-selection |
| 1 | http://blog.datadive.net/selecting-good-features-part-i-univariate-selection/ |
| 2 | http://blog.datadive.net/selecting-good-features-part-ii-linear-models-and-regularization/ |
| 3 | http://blog.datadive.net/selecting-good-features-part-iii-random-forests/ |
| 4 | http://blog.datadive.net/selecting-good-features-part-iv-stability-selection-rfe-and-everything-side-by-side/ |
| 1 | https://www.kaggle.com/residentmario/automated-feature-selection-with-sklearn |
| 2 | https://machinelearningmastery.com/feature-selection-machine-learning-python/ |
| sklearn | https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection |
| eli5 | https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html#feature-selection |
| scikit-feature | https://github.com/jundongl/scikit-feature |
| stability-selection | https://github.com/scikit-learn-contrib/stability-selection |
| scikit-rebate | https://github.com/EpistasisLab/scikit-rebate |
| scikit-genetic | https://github.com/manuel-calzolari/sklearn-genetic |
| boruta_py | https://github.com/scikit-learn-contrib/boruta_py |
| explaination | https://stats.stackexchange.com/questions/264360/boruta-all-relevant-feature-selection-vs-random-forest-variables-of-importanc/264467 |
| example | https://www.kaggle.com/tilii7/boruta-feature-elimination |
| linselect | https://github.com/efavdb/linselect |
| mlxtend | https://rasbt.github.io/mlxtend/user_guide/feature_selection/ExhaustiveFeatureSelector/ |
| BoostARoota | https://github.com/chasedehan/BoostARoota |
| INVASE | https://github.com/jsyoon0823/INVASE |
| https://github.com/indmitDS/Machine-Learning-Tutorial#dimensionality-reduction--representation-learning |
| https://github.com/indmitDS/Machine-Learning-Tutorial#selection |
| Review | https://members.loria.fr/moberger/Enseignement/AVR/Exposes/TR_Dimensiereductie.pdf |
| link | https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html |
| link | https://blog.keras.io/building-autoencoders-in-keras.html |
| link | https://scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html#sklearn.manifold.Isomap |
| link | https://scikit-learn.org/stable/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html |
| link | https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.draw_graph.html#scanpy.tl.draw_graph |
| link | https://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html |
| link | https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.diffmap.html |
| link | https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE |
| link | https://github.com/ziyuang/pynerv |
| paper | https://www.jmlr.org/papers/volume11/venna10a/venna10a.pdf |
| link | https://github.com/EpistasisLab/scikit-mdr |
| link | https://github.com/lmcinnes/umap |
| link | https://scikit-learn.org/stable/modules/random_projection.html |
| link | https://github.com/beringresearch/ivis |
| link | https://github.com/lightly-ai/lightly |
| https://github.com/indmitDS/Machine-Learning-Tutorial#neural-network-based |
| lightly | https://github.com/lightly-ai/lightly |
| esvit | https://github.com/microsoft/esvit |
| MCML | https://github.com/pachterlab/MCML |
| paper | https://www.biorxiv.org/content/10.1101/2021.08.25.457696v1 |
| https://github.com/indmitDS/Machine-Learning-Tutorial#packages |
| Talk | https://www.youtube.com/watch?v=9iol3Lk6kyU |
| tsne intro | https://distill.pub/2016/misread-tsne/ |
| sklearn.manifold | https://scikit-learn.org/stable/modules/classes.html#module-sklearn.manifold |
| sklearn.decomposition | https://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition |
| sklearn.random_projection | https://scikit-learn.org/stable/modules/random_projection.html |
| prince | https://github.com/MaxHalford/prince |
| lvdmaaten | https://lvdmaaten.github.io/tsne/ |
| MulticoreTSNE | https://github.com/DmitryUlyanov/Multicore-TSNE |
| FIt-SNE | https://github.com/KlugerLab/FIt-SNE |
| umap | https://github.com/lmcinnes/umap |
| talk | https://www.youtube.com/watch?v=nq6iPZVUxZU |
| explorer | https://github.com/GrantCuster/umap-explorer |
| explanation | https://pair-code.github.io/understanding-umap/ |
| parallel version | https://docs.rapids.ai/api/cuml/stable/api.html |
| sleepwalk | https://github.com/anders-biostat/sleepwalk/ |
| somoclu | https://github.com/peterwittek/somoclu |
| scikit-tda | https://github.com/scikit-tda/scikit-tda |
| paper | https://www.nature.com/articles/srep01236 |
| talk | https://www.youtube.com/watch?v=F2t_ytTLrQ4 |
| talk | https://www.youtube.com/watch?v=AWoeBzJd7uQ |
| paper | https://www.uncg.edu/mat/faculty/cdsmyth/topological-approaches-skin.pdf |
| giotto-tda | https://github.com/giotto-ai/giotto-tda |
| ivis | https://github.com/beringresearch/ivis |
| trimap | https://github.com/eamid/trimap |
| scanpy | https://github.com/theislab/scanpy |
| Force-directed graph drawing | https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.draw_graph.html#scanpy.tl.draw_graph |
| Diffusion Maps | https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.diffmap.html |
| direpack | https://github.com/SvenSerneels/direpack |
| DBS | https://cran.r-project.org/web/packages/DatabionicSwarm/vignettes/DatabionicSwarm.html |
| https://github.com/indmitDS/Machine-Learning-Tutorial#training-related |
| iterative-stratification | https://github.com/trent-b/iterative-stratification |
| livelossplot | https://github.com/stared/livelossplot |
| https://github.com/indmitDS/Machine-Learning-Tutorial#visualization |
| All charts | https://datavizproject.com/ |
| Austrian monuments | https://github.com/njanakiev/austrian-monuments-visualization |
| cufflinks | https://github.com/santosjorge/cufflinks |
| plotly | https://plot.ly/ |
| medium | https://towardsdatascience.com/the-next-level-of-data-visualization-in-python-dd6e99039d5e |
| example | https://github.com/WillKoehrsen/Data-Analysis/blob/master/plotly/Plotly%20Whirlwind%20Introduction.ipynb |
| physt | https://github.com/janpipek/physt |
| talk | https://www.youtube.com/watch?v=ZG-wH3-Up9Y |
| notebook | https://nbviewer.jupyter.org/github/janpipek/pydata2018-berlin/blob/master/notebooks/talk.ipynb |
| fast-histogram | https://github.com/astrofrog/fast-histogram |
| matplotlib_venn | https://github.com/konstantint/matplotlib-venn |
| alternative | https://github.com/penrose/penrose |
| joypy | https://github.com/sbebo/joypy |
| Ridge plots in seaborn | https://seaborn.pydata.org/examples/kde_ridgeplot.html |
| mosaic plots | https://www.statsmodels.org/dev/generated/statsmodels.graphics.mosaicplot.mosaic.html |
| example | https://sukhbinder.wordpress.com/2018/09/18/mosaic-plot-in-python/ |
| scikit-plot | https://github.com/reiinakano/scikit-plot |
| yellowbrick | https://github.com/DistrictDataLabs/yellowbrick |
| bokeh | https://bokeh.pydata.org/en/latest/ |
| Examples | https://bokeh.pydata.org/en/latest/docs/user_guide/server.html |
| Examples | https://github.com/WillKoehrsen/Bokeh-Python-Visualization |
| lets-plot | https://github.com/JetBrains/lets-plot/blob/master/README_PYTHON.md |
| animatplot | https://github.com/t-makaro/animatplot |
| plotnine | https://github.com/has2k1/plotnine |
| altair | https://altair-viz.github.io/ |
| bqplot | https://github.com/bloomberg/bqplot |
| hvplot | https://github.com/pyviz/hvplot |
| holoviews | http://holoviews.org/ |
| dtreeviz | https://github.com/parrt/dtreeviz |
| chartify | https://github.com/spotify/chartify/ |
| VivaGraphJS | https://github.com/anvaka/VivaGraphJS |
| pm | https://github.com/anvaka/pm |
| example | https://w2v-vis-dot-hcg-team-di.appspot.com/#/galaxy/word2vec?cx=5698&cy=-5135&cz=5923&lx=0.1127&ly=0.3238&lz=-0.1680&lw=0.9242&ml=150&s=1.75&l=1&v=hc |
| python-ternary | https://github.com/marcharper/python-ternary |
| falcon | https://github.com/uwdata/falcon |
| hiplot | https://github.com/facebookresearch/hiplot |
| visdom | https://github.com/fossasia/visdom |
| mpl-scatter-density | https://github.com/astrofrog/mpl-scatter-density |
| ComplexHeatmap | https://github.com/jokergoo/ComplexHeatmap |
| largeVis | https://github.com/elbamos/largeVis |
| https://github.com/indmitDS/Machine-Learning-Tutorial#colors |
| palettable | https://github.com/jiffyclub/palettable |
| colorbrewer2 | https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3 |
| colorcet | https://github.com/holoviz/colorcet |
| https://github.com/indmitDS/Machine-Learning-Tutorial#dashboards |
| superset | https://github.com/apache/superset |
| streamlit | https://github.com/streamlit/streamlit |
| Resources | https://github.com/marcskovmadsen/awesome-streamlit |
| Gallery | https://awesome-streamlit.org/ |
| Components | https://www.streamlit.io/components |
| bokeh-events | https://github.com/ash2shukla/streamlit-bokeh-events |
| dash | https://dash.plot.ly/gallery |
| Resources | https://github.com/ucg8j/awesome-dash |
| visdom | https://github.com/facebookresearch/visdom |
| panel | https://panel.pyviz.org/index.html |
| altair example | https://github.com/xhochy/altair-vue-vega-example |
| Video | https://www.youtube.com/watch?v=4L568emKOvs |
| voila | https://github.com/QuantStack/voila |
| https://github.com/indmitDS/Machine-Learning-Tutorial#survey-tools |
| samplics | https://github.com/samplics-org/samplics |
| https://github.com/indmitDS/Machine-Learning-Tutorial#geographical-tools |
| folium | https://github.com/python-visualization/folium |
| jupyter plugin | https://github.com/jupyter-widgets/ipyleaflet |
| gmaps | https://github.com/pbugnion/gmaps |
| stadiamaps | https://stadiamaps.com/ |
| datashader | https://github.com/bokeh/datashader |
| sklearn | https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html |
| Example | https://tech.minodes.com/experiments-with-in-memory-spatial-radius-queries-in-python-e40c9e66cf63 |
| pynndescent | https://github.com/lmcinnes/pynndescent |
| geocoder | https://github.com/DenisCarriere/geocoder |
| talk | https://www.youtube.com/watch?v=eHRggqAvczE |
| repo | https://github.com/dillongardner/PyDataSpatialAnalysis |
| geopandas | https://github.com/geopandas/geopandas |
| ipynb | https://github.com/njanakiev/osm-predict-economic-measurements/blob/master/osm-predict-economic-indicators.ipynb |
| PySal | https://github.com/pysal/pysal |
| geography | https://github.com/ushahidi/geograpy |
| cartogram | https://go-cart.io/cartogram |
| https://github.com/indmitDS/Machine-Learning-Tutorial#recommender-systems |
| 1 | https://lazyprogrammer.me/tutorial-on-collaborative-filtering-and-matrix-factorization-in-python/ |
| 2 | https://medium.com/@james_aka_yale/the-4-recommendation-engines-that-can-predict-your-movie-tastes-bbec857b8223 |
| 2-ipynb | https://github.com/khanhnamle1994/movielens/blob/master/Content_Based_and_Collaborative_Filtering_Models.ipynb |
| 3 | https://www.kaggle.com/morrisb/how-to-recommend-anything-deep-recommender |
| surprise | https://github.com/NicolasHug/Surprise |
| talk | https://www.youtube.com/watch?v=d7iIb_XVkZs |
| turicreate | https://github.com/apple/turicreate |
| implicit | https://github.com/benfred/implicit |
| spotlight | https://github.com/maciejkula/spotlight |
| lightfm | https://github.com/lyst/lightfm |
| funk-svd | https://github.com/gbolmier/funk-svd |
| pywFM | https://github.com/jfloff/pywFM |
| https://github.com/indmitDS/Machine-Learning-Tutorial#decision-tree-models |
| Intro to Decision Trees and Random Forests | https://victorzhou.com/blog/intro-to-random-forests/ |
| Intro to Gradient Boosting | http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/ |
| lightgbm | https://github.com/Microsoft/LightGBM |
| doc | https://sites.google.com/view/lauraepp/parameters |
| xgboost | https://github.com/dmlc/xgboost |
| doc | https://sites.google.com/view/lauraepp/parameters |
| link1 | https://stats.stackexchange.com/questions/255783/confidence-interval-for-xgb-forecast |
| link2 | https://towardsdatascience.com/regression-prediction-intervals-with-xgboost-428e0a018b |
| catboost | https://github.com/catboost/catboost |
| h2o | https://github.com/h2oai/h2o-3 |
| snapml | https://www.zurich.ibm.com/snapml/ |
| PyPI | https://pypi.org/project/snapml/ |
| pycaret | https://github.com/pycaret/pycaret |
| thundergbm | https://github.com/Xtra-Computing/thundergbm |
| h2o | https://github.com/h2oai/h2o-3 |
| forestci | https://github.com/scikit-learn-contrib/forest-confidence-interval |
| scikit-garden | https://github.com/scikit-garden/scikit-garden |
| grf | https://github.com/grf-labs/grf |
| dtreeviz | https://github.com/parrt/dtreeviz |
| Nuance | https://github.com/SauceCat/Nuance |
| rfpimp | https://github.com/parrt/random-forest-importances |
| link | http://explained.ai/rf-importance/index.html |
| treeinterpreter | https://github.com/andosa/treeinterpreter |
| bartpy | https://github.com/JakeColtman/bartpy |
| infiniteboost | https://github.com/arogozhnikov/infiniteboost |
| merf | https://github.com/manifoldai/merf |
| video | https://www.youtube.com/watch?v=gWj4ZwB7f3o |
| rrcf | https://github.com/kLabUM/rrcf |
| groot | https://github.com/tudelft-cda-lab/GROOT |
| linear-tree | https://github.com/cerlymarco/linear-tree |
| https://github.com/indmitDS/Machine-Learning-Tutorial#natural-language-processing-nlp--text-processing |
| talk | https://www.youtube.com/watch?v=6zm9NC9uRkk |
| nb | https://nbviewer.jupyter.org/github/skipgram/modern-nlp-in-python/blob/master/executable/Modern_NLP_in_Python.ipynb |
| nb2 | https://ahmedbesbes.com/how-to-mine-newsfeed-data-and-extract-interactive-insights-in-python.html |
| talk | https://www.youtube.com/watch?time_continue=2&v=sI7VpFNiy_I |
| Text classification Intro | https://mlwhiz.com/blog/2018/12/17/text_classification/ |
| Preprocessing blog post | https://mlwhiz.com/blog/2019/01/17/deeplearning_nlp_preprocess/ |
| gensim | https://radimrehurek.com/gensim/ |
| Example | https://markroxor.github.io/gensim/static/notebooks/gensim_news_classification.html |
| Coherence Model | https://radimrehurek.com/gensim/models/coherencemodel.html |
| GloVe | https://nlp.stanford.edu/projects/glove/ |
| 1 | https://www.kaggle.com/jhoward/improved-lstm-baseline-glove-dropout |
| 2 | https://www.kaggle.com/sbongo/do-pretrained-embeddings-give-you-the-extra-edge |
| StarSpace | https://github.com/facebookresearch/StarSpace |
| wikipedia2vec | https://wikipedia2vec.github.io/wikipedia2vec/pretrained/ |
| visualization | https://projector.tensorflow.org/ |
| magnitude | https://github.com/plasticityai/magnitude |
| pyldavis | https://github.com/bmabey/pyLDAvis |
| spaCy | https://spacy.io/ |
| NTLK | https://www.nltk.org/ |
| pytext | https://github.com/facebookresearch/PyText |
| fastText | https://github.com/facebookresearch/fastText |
| annoy | https://github.com/spotify/annoy |
| faiss | https://github.com/facebookresearch/faiss |
| pysparnn | https://github.com/facebookresearch/pysparnn |
| infomap | https://github.com/mapequation/infomap |
| example | https://github.com/mapequation/infomap/blob/master/examples/python/infomap-examples.ipynb |
| datasketch | https://github.com/ekzhu/datasketch |
| flair | https://github.com/zalandoresearch/flair |
| stanfordnlp | https://github.com/stanfordnlp/stanfordnlp |
| Chatistics | https://github.com/MasterScrat/Chatistics |
| textvec | https://github.com/textvec/textvec |
| https://github.com/indmitDS/Machine-Learning-Tutorial#papers |
| Search Engine Correlation | https://arxiv.org/pdf/1107.2691.pdf |
| https://github.com/indmitDS/Machine-Learning-Tutorial#biology |
| https://github.com/indmitDS/Machine-Learning-Tutorial#sequencing |
| scanpy | https://github.com/theislab/scanpy |
| tutorial | https://github.com/theislab/single-cell-tutorial |
| https://github.com/indmitDS/Machine-Learning-Tutorial#image-related |
| mahotas | http://luispedro.org/software/mahotas/ |
| example | https://github.com/luispedro/python-image-tutorial/blob/master/Segmenting%20cell%20images%20(fluorescent%20microscopy).ipynb |
| imagepy | https://github.com/Image-Py/imagepy |
| CellProfiler | https://github.com/CellProfiler/CellProfiler |
| imglyb | https://github.com/imglib/imglyb |
| talk | https://www.youtube.com/watch?v=Ddo5z5qGMb8 |
| slides | https://github.com/hanslovsky/scipy-2019/blob/master/scipy-2019-imglyb.pdf |
| microscopium | https://github.com/microscopium/microscopium |
| talk | https://www.youtube.com/watch?v=ytEQl9xs8FQ |
| cytokit | https://github.com/hammerlab/cytokit |
| https://github.com/indmitDS/Machine-Learning-Tutorial#image-processing |
| Talk | https://www.youtube.com/watch?v=Y5GJmnIhvFk |
| cv2 | https://github.com/skvark/opencv-python |
| Gaussian Filter | https://docs.opencv.org/3.1.0/d4/d13/tutorial_py_filtering.html |
| Morphological Transformations | https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_morphological_ops/py_morphological_ops.html |
| scikit-image | https://github.com/scikit-image/scikit-image |
| https://github.com/indmitDS/Machine-Learning-Tutorial#neural-networks |
| https://github.com/indmitDS/Machine-Learning-Tutorial#tutorials--viewer |
| Convolutional Neural Networks for Visual Recognition | https://cs231n.github.io/ |
| Lessons 1-7 | https://course.fast.ai/videos/?lesson=1 |
| Lessons 8-14 | http://course18.fast.ai/lessons/lessons2.html |
| Tensorflow without a PhD | https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd |
| Blog | https://distill.pub/2017/feature-visualization/ |
| PPT | http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf |
| Tensorflow Playground | https://playground.tensorflow.org/ |
| Visualization of optimization algorithms | https://vis.ensmallen.org/ |
| Another visualization | https://github.com/jettify/pytorch-optimizer |
| cutouts-explorer | https://github.com/mgckind/cutouts-explorer |
| https://github.com/indmitDS/Machine-Learning-Tutorial#image-related-1 |
| imgaug | https://github.com/aleju/imgaug |
| Augmentor | https://github.com/mdbloice/Augmentor |
| keras preprocessing | https://keras.io/preprocessing/image/ |
| albumentations | https://github.com/albu/albumentations |
| augmix | https://github.com/google-research/augmix |
| kornia | https://github.com/kornia/kornia |
| augly | https://github.com/facebookresearch/AugLy |
| https://github.com/indmitDS/Machine-Learning-Tutorial#lossfunction-related |
| SegLoss | https://github.com/JunMa11/SegLoss |
| https://github.com/indmitDS/Machine-Learning-Tutorial#text-related |
| ktext | https://github.com/hamelsmu/ktext |
| textgenrnn | https://github.com/minimaxir/textgenrnn |
| ctrl | https://github.com/salesforce/ctrl |
| https://github.com/indmitDS/Machine-Learning-Tutorial#libs |
| keras | https://keras.io/ |
| tensorflow | https://www.tensorflow.org/ |
| examples | https://gist.github.com/candlewill/552fa102352ccce42fd829ae26277d24 |
| timm | https://github.com/rwightman/pytorch-image-models |
| keras-contrib | https://github.com/keras-team/keras-contrib |
| keras-tuner | https://github.com/keras-team/keras-tuner |
| hyperas | https://github.com/maxpumperla/hyperas |
| elephas | https://github.com/maxpumperla/elephas |
| tflearn | https://github.com/tflearn/tflearn |
| tensorlayer | https://github.com/tensorlayer/tensorlayer |
| tricks | https://github.com/wagamamaz/tensorlayer-tricks |
| tensorforce | https://github.com/reinforceio/tensorforce |
| fastai | https://github.com/fastai/fastai |
| pytorch-optimizer | https://github.com/jettify/pytorch-optimizer |
| ignite | https://github.com/pytorch/ignite |
| skorch | https://github.com/dnouri/skorch |
| talk | https://www.youtube.com/watch?v=0J7FaLk0bmQ |
| slides | https://github.com/thomasjpfan/skorch_talk |
| autokeras | https://github.com/jhfjhfj1/autokeras |
| PlotNeuralNet | https://github.com/HarisIqbal88/PlotNeuralNet |
| lucid | https://github.com/tensorflow/lucid |
| Activation Maps | https://openai.com/blog/introducing-activation-atlases/ |
| tcav | https://github.com/tensorflow/tcav |
| AdaBound | https://github.com/Luolc/AdaBound |
| alt | https://github.com/titu1994/keras-adabound |
| foolbox | https://github.com/bethgelab/foolbox |
| hiddenlayer | https://github.com/waleedka/hiddenlayer |
| imgclsmob | https://github.com/osmr/imgclsmob |
| netron | https://github.com/lutzroeder/netron |
| torchcv | https://github.com/donnyyou/torchcv |
| pytorch-lightning | https://github.com/PyTorchLightning/PyTorch-lightning |
| lightly | https://github.com/lightly-ai/lightly |
| https://github.com/indmitDS/Machine-Learning-Tutorial#distributed-libs |
| flexflow | https://github.com/flexflow/FlexFlow |
| https://github.com/indmitDS/Machine-Learning-Tutorial#architecture-visualization |
| netron | https://github.com/lutzroeder/netron |
| https://github.com/indmitDS/Machine-Learning-Tutorial#object-detection--instance-segmentation |
| segmentation_models | https://github.com/qubvel/segmentation_models |
| yolact | https://github.com/dbolya/yolact |
| EfficientDet Pytorch | https://github.com/toandaominh1997/EfficientDet.Pytorch |
| EfficientDet Keras | https://github.com/xuannianz/EfficientDet |
| detectron2 | https://github.com/facebookresearch/detectron2 |
| simpledet | https://github.com/TuSimple/simpledet |
| CenterNet | https://github.com/xingyizhou/CenterNet |
| FCOS | https://github.com/tianzhi0549/FCOS |
| norfair | https://github.com/tryolabs/norfair |
| https://github.com/indmitDS/Machine-Learning-Tutorial#image-annotation |
| cvat | https://github.com/openvinotoolkit/cvat |
| pigeon | https://github.com/agermanidis/pigeon |
| https://github.com/indmitDS/Machine-Learning-Tutorial#image-classification |
| nfnets | https://github.com/ypeleg/nfnets-keras |
| efficientnet | https://github.com/lukemelas/EfficientNet-PyTorch |
| pycls | https://github.com/facebookresearch/pycls |
| https://github.com/indmitDS/Machine-Learning-Tutorial#applications-and-snippets |
| CycleGAN and Pix2pix | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix |
| SPADE | https://github.com/nvlabs/spade |
| Entity Embeddings of Categorical Variables | https://arxiv.org/abs/1604.06737 |
| code | https://github.com/entron/entity-embedding-rossmann |
| kaggle | https://www.kaggle.com/aquatic/entity-embedding-neural-net/code |
| Image Super-Resolution | https://github.com/idealo/image-super-resolution |
| Talk | https://www.youtube.com/watch?v=dVFZpodqJiI |
| 1 | https://www.thomasjpfan.com/2018/07/nuclei-image-segmentation-tutorial/ |
| 2 | https://www.thomasjpfan.com/2017/08/hassle-free-unets/ |
| deeplearning-models | https://github.com/rasbt/deeplearning-models |
| https://github.com/indmitDS/Machine-Learning-Tutorial#variational-autoencoders-vae |
| disentanglement_lib | https://github.com/google-research/disentanglement_lib |
| https://github.com/indmitDS/Machine-Learning-Tutorial#graph-based-neural-networks |
| How to do Deep Learning on Graphs with Graph Convolutional Networks | https://towardsdatascience.com/how-to-do-deep-learning-on-graphs-with-graph-convolutional-networks-7d2250723780 |
| Introduction To Graph Convolutional Networks | http://tkipf.github.io/graph-convolutional-networks/ |
| An attempt at demystifying graph deep learning | https://ericmjl.github.io/essays-on-data-science/machine-learning/graph-nets/ |
| ogb | https://ogb.stanford.edu/ |
| networkx | https://github.com/networkx/networkx |
| cugraph | https://github.com/rapidsai/cugraph |
| pytorch-geometric | https://github.com/rusty1s/pytorch_geometric |
| dgl | https://github.com/dmlc/dgl |
| graph_nets | https://github.com/deepmind/graph_nets |
| https://github.com/indmitDS/Machine-Learning-Tutorial#other-neural-network-and-deep-learning-frameworks |
| caffe | https://github.com/BVLC/caffe |
| pretrained models | https://github.com/BVLC/caffe/wiki/Model-Zoo |
| mxnet | https://github.com/apache/incubator-mxnet |
| book | https://d2l.ai/index.html |
| https://github.com/indmitDS/Machine-Learning-Tutorial#model-conversion |
| hummingbird | https://github.com/microsoft/hummingbird |
| https://github.com/indmitDS/Machine-Learning-Tutorial#gpu |
| cuML | https://github.com/rapidsai/cuml |
| Intro | https://www.youtube.com/watch?v=6XzS5XcpicM&t=2m50s |
| thundergbm | https://github.com/Xtra-Computing/thundergbm |
| thundersvm | https://github.com/Xtra-Computing/thundersvm |
| video | https://www.youtube.com/watch?v=Jxxs_moibog |
| https://github.com/indmitDS/Machine-Learning-Tutorial#regression |
| slides | https://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf |
| forum | https://www.quora.com/How-does-support-vector-regression-work |
| paper | http://alex.smola.org/papers/2003/SmoSch03b.pdf |
| pyearth | https://github.com/scikit-learn-contrib/py-earth |
| tutorial | https://uc-r.github.io/mars |
| pygam | https://github.com/dswah/pyGAM |
| Explanation | https://multithreaded.stitchfix.com/blog/2015/07/30/gam/ |
| GLRM | https://github.com/madeleineudell/LowRankModels.jl |
| tweedie | https://xgboost.readthedocs.io/en/latest/parameter.html#parameters-for-tweedie-regression-objective-reg-tweedie |
| Talk | https://www.youtube.com/watch?v=-o0lpHBq85I |
| https://github.com/indmitDS/Machine-Learning-Tutorial#classification |
| Talk | https://www.youtube.com/watch?v=DkLPYccEJ8Y |
| Notebook | https://github.com/ianozsvald/data_science_delivered/blob/master/ml_creating_correct_capable_classifiers.ipynb |
| Blog post: Probability Scoring | https://machinelearningmastery.com/how-to-score-probability-predictions-in-python/ |
| All classification metrics | http://rali.iro.umontreal.ca/rali/sites/default/files/publis/SokolovaLapalme-JIPM09.pdf |
| DESlib | https://github.com/scikit-learn-contrib/DESlib |
| human-learn | https://github.com/koaning/human-learn |
| https://github.com/indmitDS/Machine-Learning-Tutorial#metric-learning |
| Contrastive Representation Learning | https://lilianweng.github.io/lil-log/2021/05/31/contrastive-representation-learning.html |
| metric-learn | https://github.com/scikit-learn-contrib/metric-learn |
| pytorch-metric-learning | https://github.com/KevinMusgrave/pytorch-metric-learning |
| deep_metric_learning | https://github.com/ronekko/deep_metric_learning |
| ivis | https://bering-ivis.readthedocs.io/en/latest/supervised.html |
| https://github.com/indmitDS/Machine-Learning-Tutorial#distance-functions |
| scipy.spatial | https://docs.scipy.org/doc/scipy/reference/spatial.distance.html |
| pyemd | https://github.com/wmayner/pyemd |
| OpenCV implementation | https://docs.opencv.org/3.4/d6/dc7/group__imgproc__hist.html |
| POT implementation | https://pythonot.github.io/auto_examples/plot_OT_2D_samples.html |
| dcor | https://github.com/vnmabus/dcor |
| GeomLoss | https://www.kernel-operations.io/geomloss/ |
| https://github.com/indmitDS/Machine-Learning-Tutorial#clustering |
| Overview of clustering algorithms applied image data (= Deep Clustering) | https://deepnotes.io/deep-clustering |
| Clustering with Deep Learning: Taxonomy and New Methods | https://arxiv.org/pdf/1801.07648.pdf |
| hdbscan | https://github.com/scikit-learn-contrib/hdbscan |
| talk | https://www.youtube.com/watch?v=dGsxd67IFiU |
| blog | https://towardsdatascience.com/understanding-hdbscan-and-density-based-clustering-121dbee1320e |
| pyclustering | https://github.com/annoviko/pyclustering |
| FCPS | https://github.com/Mthrun/FCPS |
| GaussianMixture | https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html |
| video | https://www.youtube.com/watch?v=aICqoAG5BXQ |
| nmslib | https://github.com/nmslib/nmslib |
| buckshotpp | https://github.com/zjohn77/buckshotpp |
| merf | https://github.com/manifoldai/merf |
| video | https://www.youtube.com/watch?v=gWj4ZwB7f3o |
| tree-SNE | https://github.com/isaacrob/treesne |
| MiniSom | https://github.com/JustGlowing/minisom |
| distribution_clustering | https://github.com/EricElmoznino/distribution_clustering |
| paper | https://arxiv.org/abs/1804.02624 |
| related paper | https://arxiv.org/abs/2003.07770 |
| alt | https://github.com/r0f1/distribution_clustering |
| phenograph | https://github.com/dpeerlab/phenograph |
| https://github.com/indmitDS/Machine-Learning-Tutorial#clustering-evalutation |
| Wagner, Wagner - Comparing Clusterings - An Overview | https://publikationen.bibliothek.kit.edu/1000011477/812079 |
| Adjusted Rand Index | https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html |
| Normalized Mutual Information | https://scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html |
| Adjusted Mutual Information | https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html |
| Fowlkes-Mallows Score | https://scikit-learn.org/stable/modules/generated/sklearn.metrics.fowlkes_mallows_score.html |
| Silhouette Coefficient | https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html |
| Variation of Information | https://gist.github.com/jwcarr/626cbc80e0006b526688 |
| Julia | https://clusteringjl.readthedocs.io/en/latest/varinfo.html |
| Pair Confusion Matrix | https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html |
| Consensus Score | https://scikit-learn.org/stable/modules/generated/sklearn.metrics.consensus_score.html |
| Assessing the quality of a clustering (video) | https://www.youtube.com/watch?v=Mf6MqIS2ql4 |
| fpc | https://cran.r-project.org/web/packages/fpc/index.html |
| https://github.com/indmitDS/Machine-Learning-Tutorial#interpretable-classifiers-and-regressors |
| skope-rules | https://github.com/scikit-learn-contrib/skope-rules |
| sklearn-expertsys | https://github.com/tmadl/sklearn-expertsys |
| https://github.com/indmitDS/Machine-Learning-Tutorial#multi-label-classification |
| scikit-multilearn | https://github.com/scikit-multilearn/scikit-multilearn |
| talk | https://www.youtube.com/watch?v=m-tAASQA7XQ&t=18m57s |
| https://github.com/indmitDS/Machine-Learning-Tutorial#signal-processing-and-filtering |
| Stanford Lecture Series on Fourier Transformation | https://see.stanford.edu/Course/EE261 |
| Youtube | https://www.youtube.com/watch?v=gZNm7L96pfY&list=PLB24BC7956EE040CD&index=1 |
| Lecture Notes | https://see.stanford.edu/materials/lsoftaee261/book-fall-07.pdf |
| The Scientist & Engineer's Guide to Digital Signal Processing (1999) | https://www.analog.com/en/education/education-library/scientist_engineers_guide.html |
| Kalman Filter book | https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python |
| Interactive Tool | https://fiiir.com/ |
| Examples | https://plot.ly/python/fft-filters/ |
| filterpy | https://github.com/rlabbe/filterpy |
| https://github.com/indmitDS/Machine-Learning-Tutorial#time-series |
| statsmodels | https://www.statsmodels.org/dev/tsa.html |
| seasonal decompose | https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.seasonal_decompose.html |
| example | https://gist.github.com/balzer82/5cec6ad7adc1b550e7ee |
| SARIMA | https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html |
| granger causality | http://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.grangercausalitytests.html |
| kats | https://github.com/facebookresearch/kats |
| prophet | https://github.com/facebook/prophet |
| pyramid | https://github.com/tgsmith61591/pyramid |
| pmdarima | https://github.com/tgsmith61591/pmdarima |
| pyflux | https://github.com/RJT1990/pyflux |
| atspy | https://github.com/firmai/atspy |
| pm-prophet | https://github.com/luke14free/pm-prophet |
| htsprophet | https://github.com/CollinRooney12/htsprophet |
| nupic | https://github.com/numenta/nupic |
| tensorflow | https://github.com/tensorflow/tensorflow/ |
| link | https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/ |
| link | https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/timeseries |
| link | https://github.com/hzy46/TensorFlow-Time-Series-Examples |
| Explain LSTM | https://github.com/slundberg/shap/blob/master/notebooks/deep_explainer/Keras%20LSTM%20for%20IMDB%20Sentiment%20Classification.ipynb |
| 1 | https://machinelearningmastery.com/how-to-develop-lstm-models-for-multi-step-time-series-forecasting-of-household-power-consumption/ |
| 2 | https://github.com/guillaume-chevalier/seq2seq-signal-prediction |
| 3 | https://github.com/JEddy92/TimeSeries_Seq2Seq/blob/master/notebooks/TS_Seq2Seq_Intro.ipynb |
| 4 | https://github.com/LukeTonin/keras-seq-2-seq-signal-prediction |
| tspreprocess | https://github.com/MaxBenChrist/tspreprocess |
| tsfresh | https://github.com/blue-yonder/tsfresh |
| thunder | https://github.com/thunder-project/thunder |
| gatspy | https://www.astroml.org/gatspy/ |
| talk | https://www.youtube.com/watch?v=E4NMZyfao2c |
| gendis | https://github.com/IBCNServices/GENDIS |
| example | https://github.com/IBCNServices/GENDIS/blob/master/gendis/example.ipynb |
| tslearn | https://github.com/rtavenar/tslearn |
| pastas | https://pastas.readthedocs.io/en/latest/examples.html |
| fastdtw | https://github.com/slaypni/fastdtw |
| fable | https://www.rdocumentation.org/packages/fable/versions/0.0.0.9000 |
| CausalImpact | https://github.com/tcassou/causal_impact |
| R package | https://google.github.io/CausalImpact/CausalImpact.html |
| pydlm | https://github.com/wwrechard/pydlm |
| R package | https://cran.r-project.org/web/packages/bsts/index.html |
| Blog post | http://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.html |
| PyAF | https://github.com/antoinecarme/pyaf |
| luminol | https://github.com/linkedin/luminol |
| matrixprofile-ts | https://github.com/target/matrixprofile-ts |
| website | https://www.cs.ucr.edu/~eamonn/MatrixProfile.html |
| ppt | https://www.cs.ucr.edu/~eamonn/Matrix_Profile_Tutorial_Part1.pdf |
| alternative | https://github.com/matrix-profile-foundation/mass-ts |
| stumpy | https://github.com/TDAmeritrade/stumpy |
| obspy | https://github.com/obspy/obspy |
| RobustSTL | https://github.com/LeeDoYup/RobustSTL |
| seglearn | https://github.com/dmbee/seglearn |
| pyts | https://github.com/johannfaouzi/pyts |
| Imaging time series | https://pyts.readthedocs.io/en/latest/auto_examples/index.html#imaging-time-series |
| example | https://gist.github.com/oguiza/c9c373aec07b96047d1ba484f23b7b47 |
| example | https://github.com/kiss90/time-series-classification |
| sktime | https://github.com/alan-turing-institute/sktime |
| sktime-dl | https://github.com/uea-machine-learning/sktime-dl |
| adtk | https://github.com/arundo/adtk |
| rocket | https://github.com/angus924/rocket |
| luminaire | https://github.com/zillow/luminaire |
| https://github.com/indmitDS/Machine-Learning-Tutorial#time-series-evaluation |
| TimeSeriesSplit | https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html |
| tscv | https://github.com/WenjieZ/TSCV |
| https://github.com/indmitDS/Machine-Learning-Tutorial#financial-data |
| 1 | https://calmcode.io/cvxpy-one/the-stigler-diet.html |
| 2 | https://calmcode.io/cvxpy-two/introduction.html |
| pandas-datareader | https://pandas-datareader.readthedocs.io/en/latest/whatsnew.html |
| yfinance | https://github.com/ranaroussi/yfinance |
| findatapy | https://github.com/cuemacro/findatapy |
| ta | https://github.com/bukosabino/ta |
| backtrader | https://github.com/mementum/backtrader |
| surpriver | https://github.com/tradytics/surpriver |
| ffn | https://github.com/pmorissette/ffn |
| bt | https://github.com/pmorissette/bt |
| alpaca-trade-api-python | https://github.com/alpacahq/alpaca-trade-api-python |
| eiten | https://github.com/tradytics/eiten |
| tf-quant-finance | https://github.com/google/tf-quant-finance |
| quantstats | https://github.com/ranaroussi/quantstats |
| https://github.com/indmitDS/Machine-Learning-Tutorial#quantopian-stack |
| pyfolio | https://github.com/quantopian/pyfolio |
| zipline | https://github.com/quantopian/zipline |
| alphalens | https://github.com/quantopian/alphalens |
| empyrical | https://github.com/quantopian/empyrical |
| trading_calendars | https://github.com/quantopian/trading_calendars |
| https://github.com/indmitDS/Machine-Learning-Tutorial#survival-analysis |
| Time-dependent Cox Model in R | https://stats.stackexchange.com/questions/101353/cox-regression-with-time-varying-covariates |
| lifelines | https://lifelines.readthedocs.io/en/latest/ |
| talk | https://www.youtube.com/watch?v=aKZQUaNHYb0 |
| talk2 | https://www.youtube.com/watch?v=fli-yE5grtY |
| scikit-survival | https://github.com/sebp/scikit-survival |
| xgboost | https://github.com/dmlc/xgboost |
| NHANES example | https://slundberg.github.io/shap/notebooks/NHANES%20I%20Survival%20Model.html |
| survivalstan | https://github.com/hammerlab/survivalstan |
| intro | http://www.hammerlab.org/2017/06/26/introducing-survivalstan/ |
| convoys | https://github.com/better/convoys |
| pysurvival | https://github.com/square/pysurvival |
| https://github.com/indmitDS/Machine-Learning-Tutorial#outlier-detection--anomaly-detection |
| sklearn | https://scikit-learn.org/stable/modules/outlier_detection.html |
| pyod | https://pyod.readthedocs.io/en/latest/pyod.html |
| eif | https://github.com/sahandha/eif |
| AnomalyDetection | https://github.com/twitter/AnomalyDetection |
| luminol | https://github.com/linkedin/luminol |
| Talk | https://www.youtube.com/watch?v=U7xdiGc7IRU |
| Kolmogorov-Smirnov | https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.ks_2samp.html |
| Wasserstein | https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wasserstein_distance.html |
| Energy Distance (Cramer) | https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.energy_distance.html |
| Kullback-Leibler divergence | https://scipy.github.io/devdocs/generated/scipy.stats.entropy.html |
| banpei | https://github.com/tsurubee/banpei |
| telemanom | https://github.com/khundman/telemanom |
| luminaire | https://github.com/zillow/luminaire |
| https://github.com/indmitDS/Machine-Learning-Tutorial#ranking |
| lightning | https://github.com/scikit-learn-contrib/lightning |
| https://github.com/indmitDS/Machine-Learning-Tutorial#scoring |
| SLIM | https://github.com/ustunb/slim-python |
| https://github.com/indmitDS/Machine-Learning-Tutorial#probabilistic-modeling-and-bayes |
| Intro | https://erikbern.com/2018/10/08/the-hackers-guide-to-uncertainty-estimates.html |
| Guide | https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers |
| PyMC3 | https://docs.pymc.io/ |
| intro | https://docs.pymc.io/notebooks/getting_started |
| pomegranate | https://github.com/jmschrei/pomegranate |
| talk | https://www.youtube.com/watch?v=dE5j6NW-Kzg |
| pmlearn | https://github.com/pymc-learn/pymc-learn |
| arviz | https://github.com/arviz-devs/arviz |
| zhusuan | https://github.com/thu-ml/zhusuan |
| dowhy | https://github.com/Microsoft/dowhy |
| edward | https://github.com/blei-lab/edward |
| Mixture Density Networks (MNDs) | http://edwardlib.org/tutorials/mixture-density-network |
| MDN Explanation | https://towardsdatascience.com/a-hitchhikers-guide-to-mixture-density-networks-76b435826cca |
| Pyro | https://github.com/pyro-ppl/pyro |
| tensorflow probability | https://github.com/tensorflow/probability |
| talk | https://www.youtube.com/watch?v=BrwKURU-wpk |
| example | https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Ch1_Introduction_TFP.ipynb |
| bambi | https://github.com/bambinos/bambi |
| neural-tangents | https://github.com/google/neural-tangents |
| https://github.com/indmitDS/Machine-Learning-Tutorial#gaussian-processes |
| Visualization | http://www.infinitecuriosity.org/vizgp/ |
| Article | https://distill.pub/2019/visual-exploration-gaussian-processes/ |
| GPyOpt | https://github.com/SheffieldML/GPyOpt |
| GPflow | https://github.com/GPflow/GPflow |
| gpytorch | https://gpytorch.ai/ |
| https://github.com/indmitDS/Machine-Learning-Tutorial#stacking-models-and-ensembles |
| Model Stacking Blog Post | http://blog.kaggle.com/2017/06/15/stacking-made-easy-an-introduction-to-stacknet-by-competitions-grandmaster-marios-michailidis-kazanova/ |
| mlxtend | https://github.com/rasbt/mlxtend |
| vecstack | https://github.com/vecxoz/vecstack |
| StackNet | https://github.com/kaz-Anova/StackNet |
| mlens | https://github.com/flennerhag/mlens |
| combo | https://github.com/yzhao062/combo |
| https://github.com/indmitDS/Machine-Learning-Tutorial#model-evaluation |
| pycm | https://github.com/sepandhaghighi/pycm |
| pandas_ml | https://github.com/pandas-ml/pandas-ml |
| link | http://www.ritchieng.com/machinelearning-learning-curve/ |
| yellowbrick | http://www.scikit-yb.org/en/latest/api/model_selection/learning_curve.html |
| https://github.com/indmitDS/Machine-Learning-Tutorial#model-uncertainty |
| uncertainty-toolbox | https://github.com/uncertainty-toolbox/uncertainty-toolbox |
| https://github.com/indmitDS/Machine-Learning-Tutorial#model-explanation-interpretability-feature-importance |
| Book | https://christophm.github.io/interpretable-ml-book/agnostic.html |
| Examples | https://github.com/jphall663/interpretable_machine_learning_with_python |
| shap | https://github.com/slundberg/shap |
| talk | https://www.youtube.com/watch?v=C80SQe16Rao |
| treeinterpreter | https://github.com/andosa/treeinterpreter |
| lime | https://github.com/marcotcr/lime |
| talk | https://www.youtube.com/watch?v=C80SQe16Rao |
| Warning (Myth 7) | https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-research/ |
| lime_xgboost | https://github.com/jphall663/lime_xgboost |
| eli5 | https://github.com/TeamHG-Memex/eli5 |
| lofo-importance | https://github.com/aerdem4/lofo-importance |
| talk | https://www.youtube.com/watch?v=zqsQ2ojj7sE |
| 1 | https://www.kaggle.com/divrikwicky/pf-f-lofo-importance-on-adversarial-validation |
| 2 | https://www.kaggle.com/divrikwicky/lofo-importance |
| 3 | https://www.kaggle.com/divrikwicky/santanderctp-lofo-feature-importance |
| pybreakdown | https://github.com/MI2DataLab/pyBreakDown |
| FairML | https://github.com/adebayoj/fairml |
| pycebox | https://github.com/AustinRochford/PyCEbox |
| pdpbox | https://github.com/SauceCat/PDPbox |
| example | https://www.kaggle.com/dansbecker/partial-plots |
| partial_dependence | https://github.com/nyuvis/partial_dependence |
| skater | https://github.com/datascienceinc/Skater |
| anchor | https://github.com/marcotcr/anchor |
| l2x | https://github.com/Jianbo-Lab/L2X |
| contrastive_explanation | https://github.com/MarcelRobeer/ContrastiveExplanation |
| DrWhy | https://github.com/ModelOriented/DrWhy |
| lucid | https://github.com/tensorflow/lucid |
| xai | https://github.com/EthicalML/XAI |
| innvestigate | https://github.com/albermax/innvestigate |
| dalex | https://github.com/pbiecek/DALEX |
| interpret | https://github.com/microsoft/interpret |
| causalml | https://github.com/uber/causalml |
| https://github.com/indmitDS/Machine-Learning-Tutorial#automated-machine-learning |
| AdaNet | https://github.com/tensorflow/adanet |
| tpot | https://github.com/EpistasisLab/tpot |
| auto_ml | https://github.com/ClimbsRocks/auto_ml |
| autokeras | https://github.com/jhfjhfj1/autokeras |
| nni | https://github.com/Microsoft/nni |
| automl-gs | https://github.com/minimaxir/automl-gs |
| mljar | https://github.com/mljar/mljar-supervised |
| automl_zero | https://github.com/google-research/google-research/tree/master/automl_zero |
| https://github.com/indmitDS/Machine-Learning-Tutorial#graph-representation-learning |
| Karate Club | https://github.com/benedekrozemberczki/karateclub |
| Pytorch Geometric | https://github.com/rusty1s/pytorch_geometric |
| DLG | https://github.com/dmlc/dgl |
| https://github.com/indmitDS/Machine-Learning-Tutorial#convex-optimization |
| cvxpy | https://github.com/cvxgrp/cvxpy |
| 1 | https://calmcode.io/cvxpy-one/the-stigler-diet.html |
| 2 | https://calmcode.io/cvxpy-two/introduction.html |
| https://github.com/indmitDS/Machine-Learning-Tutorial#evolutionary-algorithms--optimization |
| deap | https://github.com/DEAP/deap |
| evol | https://github.com/godatadriven/evol |
| talk | https://www.youtube.com/watch?v=68ABAU_V8qI&t=11m49s |
| platypus | https://github.com/Project-Platypus/Platypus |
| autograd | https://github.com/HIPS/autograd |
| nevergrad | https://github.com/facebookresearch/nevergrad |
| gplearn | https://gplearn.readthedocs.io/en/stable/ |
| blackbox | https://github.com/paulknysh/blackbox |
| paper | https://www.nature.com/articles/s41598-017-06645-7 |
| DeepSwarm | https://github.com/Pattio/DeepSwarm |
| https://github.com/indmitDS/Machine-Learning-Tutorial#hyperparameter-tuning |
| sklearn | https://scikit-learn.org/stable/index.html |
| GridSearchCV | https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html |
| RandomizedSearchCV | https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html |
| sklearn-deap | https://github.com/rsteca/sklearn-deap |
| hyperopt | https://github.com/hyperopt/hyperopt |
| hyperopt-sklearn | https://github.com/hyperopt/hyperopt-sklearn |
| optuna | https://github.com/pfnet/optuna |
| Talk | https://www.youtube.com/watch?v=tcrcLRopTX0 |
| skopt | https://scikit-optimize.github.io/ |
| tune | https://ray.readthedocs.io/en/latest/tune.html |
| hypergraph | https://github.com/aljabr0/hypergraph |
| bbopt | https://github.com/evhub/bbopt |
| dragonfly | https://github.com/dragonfly/dragonfly |
| botorch | https://github.com/pytorch/botorch |
| ax | https://github.com/facebook/Ax |
| https://github.com/indmitDS/Machine-Learning-Tutorial#incremental-learning-online-learning |
| PassiveAggressiveClassifier | https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html |
| PassiveAggressiveRegressor | https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html |
| creme-ml | https://github.com/creme-ml/creme |
| talk | https://www.youtube.com/watch?v=P3M6dt7bY9U |
| Kaggler | https://github.com/jeongyoonlee/Kaggler |
| onelearn | https://github.com/onelearn/onelearn |
| https://github.com/indmitDS/Machine-Learning-Tutorial#active-learning |
| Talk | https://www.youtube.com/watch?v=0efyjq5rWS4 |
| modAL | https://github.com/modAL-python/modAL |
| https://github.com/indmitDS/Machine-Learning-Tutorial#reinforcement-learning |
| YouTube | https://www.youtube.com/playlist?list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT |
| YouTube | https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs |
| 1 | https://jeffbradberry.com/posts/2015/09/intro-to-monte-carlo-tree-search/ |
| 2 | http://mcts.ai/about/index.html |
| 3 | https://medium.com/@quasimik/monte-carlo-tree-search-applied-to-letterpress-34f41c86e238 |
| 1 | https://github.com/AppliedDataSciencePartners/DeepReinforcementLearning |
| 2 | https://web.stanford.edu/~surag/posts/alphazero.html |
| 3 | https://github.com/suragnair/alpha-zero-general |
| Cheat Sheet | https://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0 |
| RLLib | https://ray.readthedocs.io/en/latest/rllib.html |
| Horizon | https://github.com/facebookresearch/Horizon/ |
| https://github.com/indmitDS/Machine-Learning-Tutorial#deployment-and-lifecycle-management |
| https://github.com/indmitDS/Machine-Learning-Tutorial#docker |
| Reduce size of docker images (video) | https://www.youtube.com/watch?v=Z1Al4I4Os_A |
| https://github.com/indmitDS/Machine-Learning-Tutorial#dependency-management |
| dephell | https://github.com/dephell/dephell |
| poetry | https://github.com/python-poetry/poetry |
| pyup | https://github.com/pyupio/pyup |
| pypi-timemachine | https://github.com/astrofrog/pypi-timemachine |
| https://github.com/indmitDS/Machine-Learning-Tutorial#data-versioning-and-pipelines |
| dvc | https://github.com/iterative/dvc |
| hangar | https://github.com/tensorwerk/hangar-py |
| kedro | https://github.com/quantumblacklabs/kedro |
| https://github.com/indmitDS/Machine-Learning-Tutorial#data-science-related |
| m2cgen | https://github.com/BayesWitnesses/m2cgen |
| sklearn-porter | https://github.com/nok/sklearn-porter |
| mlflow | https://mlflow.org/ |
| modelchimp | https://github.com/ModelChimp/modelchimp |
| skll | https://github.com/EducationalTestingService/skll |
| BentoML | https://github.com/bentoml/BentoML |
| dagster | https://github.com/dagster-io/dagster |
| knockknock | https://github.com/huggingface/knockknock |
| metaflow | https://github.com/Netflix/metaflow |
| cortex | https://github.com/cortexlabs/cortex |
| https://github.com/indmitDS/Machine-Learning-Tutorial#math-and-background |
| All kinds of math and statistics resources | https://realnotcomplex.com/ |
| Linear Algebra | https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/index.htm |
| Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
| https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/ |
| https://github.com/indmitDS/Machine-Learning-Tutorial#other |
| daft | https://github.com/dfm/daft |
| unyt | https://github.com/yt-project/unyt |
| scrapy | https://github.com/scrapy/scrapy |
| VowpalWabbit | https://github.com/VowpalWabbit/vowpal_wabbit |
| https://github.com/indmitDS/Machine-Learning-Tutorial#general-python-programming |
| more_itertools | https://more-itertools.readthedocs.io/en/latest/ |
| funcy | https://github.com/Suor/funcy |
| dateparser | https://dateparser.readthedocs.io/en/latest/ |
| jellyfish | https://github.com/jamesturk/jellyfish |
| coloredlogs | https://github.com/xolox/python-coloredlogs |
| https://github.com/indmitDS/Machine-Learning-Tutorial#resources |
| Distill.pub | https://distill.pub/ |
| Machine Learning Videos | https://github.com/dustinvtran/ml-videos |
| Data Science Notebooks | https://github.com/donnemartin/data-science-ipython-notebooks |
| Recommender Systems (Microsoft) | https://github.com/Microsoft/Recommenders |
| The GAN Zoo | https://github.com/hindupuravinash/the-gan-zoo |
| Datascience Cheatsheets | https://github.com/FavioVazquez/ds-cheatsheets |
| https://github.com/indmitDS/Machine-Learning-Tutorial#list-of-books |
| Mat Kelceys list of cool machine learning books | http://matpalm.com/blog/cool_machine_learning_books/ |
| https://github.com/indmitDS/Machine-Learning-Tutorial#other-awesome-lists |
| Awesome Adversarial Machine Learning | https://github.com/yenchenlin/awesome-adversarial-machine-learning |
| Awesome AI Booksmarks | https://github.com/goodrahstar/my-awesome-AI-bookmarks |
| Awesome AI on Kubernetes | https://github.com/CognonicLabs/awesome-AI-kubernetes |
| Awesome Big Data | https://github.com/onurakpolat/awesome-bigdata |
| Awesome Business Machine Learning | https://github.com/firmai/business-machine-learning |
| Awesome Causality | https://github.com/rguo12/awesome-causality-algorithms |
| Awesome Community Detection | https://github.com/benedekrozemberczki/awesome-community-detection |
| Awesome CSV | https://github.com/secretGeek/AwesomeCSV |
| Awesome Data Science with Ruby | https://github.com/arbox/data-science-with-ruby |
| Awesome Dash | https://github.com/ucg8j/awesome-dash |
| Awesome Decision Trees | https://github.com/benedekrozemberczki/awesome-decision-tree-papers |
| Awesome Deep Learning | https://github.com/ChristosChristofidis/awesome-deep-learning |
| Awesome ETL | https://github.com/pawl/awesome-etl |
| Awesome Financial Machine Learning | https://github.com/firmai/financial-machine-learning |
| Awesome Fraud Detection | https://github.com/benedekrozemberczki/awesome-fraud-detection-papers |
| Awesome GAN Applications | https://github.com/nashory/gans-awesome-applications |
| Awesome Graph Classification | https://github.com/benedekrozemberczki/awesome-graph-classification |
| Awesome Gradient Boosting | https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers |
| Awesome Machine Learning | https://github.com/josephmisiti/awesome-machine-learning#python |
| Awesome Machine Learning Interpretability | https://github.com/jphall663/awesome-machine-learning-interpretability |
| Awesome Machine Learning Operations | https://github.com/EthicalML/awesome-machine-learning-operations |
| Awesome Metric Learning | https://github.com/kdhht2334/Survey_of_Deep_Metric_Learning |
| Awesome Monte Carlo Tree Search | https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers |
| Awesome Online Machine Learning | https://github.com/MaxHalford/awesome-online-machine-learning |
| Awesome Pipeline | https://github.com/pditommaso/awesome-pipeline |
| Awesome Public APIs | https://github.com/public-apis/public-apis |
| Awesome Python | https://github.com/vinta/awesome-python |
| Awesome Python Data Science | https://github.com/krzjoa/awesome-python-datascience |
| Awesome Python Data Science | https://github.com/thomasjpfan/awesome-python-data-science |
| Awesome Python Data Science | https://github.com/amitness/toolbox |
| Awesome Pytorch | https://github.com/bharathgs/Awesome-pytorch-list |
| Awesome Recommender Systems | https://github.com/grahamjenson/list_of_recommender_systems |
| Awesome Semantic Segmentation | https://github.com/mrgloom/awesome-semantic-segmentation |
| Awesome Sentence Embedding | https://github.com/Separius/awesome-sentence-embedding |
| Awesome Time Series | https://github.com/MaxBenChrist/awesome_time_series_in_python |
| Awesome Time Series Anomaly Detection | https://github.com/rob-med/awesome-TS-anomaly-detection |
| https://github.com/indmitDS/Machine-Learning-Tutorial#things-i-google-a-lot |
| Color codes | https://github.com/d3/d3-3.x-api-reference/blob/master/Ordinal-Scales.md#categorical-colors |
| Frequency codes for time series | https://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases |
| Date parsing codes | https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior |
| Feature Calculators tsfresh | https://github.com/blue-yonder/tsfresh/blob/master/tsfresh/feature_extraction/feature_calculators.py |
| https://github.com/indmitDS/Machine-Learning-Tutorial#contributing |
| contribution guidelines | https://github.com/indmitDS/Machine-Learning-Tutorial/blob/main/CONTRIBUTING.md |
| https://github.com/indmitDS/Machine-Learning-Tutorial#license |
| https://creativecommons.org/publicdomain/zero/1.0/ |
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