Saravanan666/pypi-org huggingface dataset:
Package: skippex
SkippexSkippex skips intros automatically for you on Plex, with support for the
Chromecast.IMPORTANT NOTE: This is still pretty much beta software. Expect bugs and
please report them!InstallationInstalling Skippex throughDockeris the easiest way to get started:$dockerpullghcr.io/sprt/skippexDocker-compose example coming soon.If you prefer not to use Docker, you can also usepipx, which will
install Skippex in its own virtual environment:$pipxinstallskippexOr you can just usepip:$pipinstall--userskippexUsageThe first time you use Skippex, you'll first have to authorize the application
with Plex using the following command. This will open a new tab in your Web
browser allowing you to authenticate and authorize the application to access
your Plex account.Dockerpipx & pip$ docker run -v skippex:/data --network host ghcr.io/sprt/skippex auth$ skippex authOnce that's done, you can simply run Skippex and it'll start monitoring your
playback sessions and automatically skip intros for you on supported devices:Dockerpipx & pip$ docker run -v skippex:/data --network host ghcr.io/sprt/skippex run$ skippex runEt voilà! When this command says "Ready", Skippex is monitoring your shows and
will automatically skip intros for you.Note: Due to aChromecast limitation, the Docker container
has to run with host mode networking.Things to knowClients need to have "Advertise as player" enabled.Only works for players on the local network.Only skips once per playback session.Solely based on the intro markers detected by Plex; Skippex does not attempt
to detect intros itself.Tested and supported playersPlex Web AppPlex for iOS (both iPhone and iPad)Chromecast v3The NVIDIA SHIELD might be supported as well, but I don't have one so I can't
test it. Other players might also be supported. In any case, please inform me
bycreating a new issue, so I can add your player to this list.Known issuesWith a Chromecast, when seeking to a position, the WebSocket only receives
the notification 10 seconds later. Likewise, the HTTP API starts returning
the correct position only after 10 seconds. This means that if, before the
intro, the user seeks to within 10 seconds of the intro, they may view it for
a few seconds (before the notification comes in and saves us).One workaround would be to listen to Chromecast status updates usingpychromecast, but that would necessitate a rearchitecture of the code.
Package: skippy
Theskippypython packageSkip the boilerplate of scikit-learn machine learning examples.InstallationpipinstallskippyUsageIn a shell environment, you can runskippywith no arguments to perform a Logistic Regression
on thedigitsdataset.This will produce a 10 x 10 confusion matrix
with the Accuracy Score at the top.You can also pass arguments to skippy at the command line.For example,skippy-datadiabetes-typelinear_model-nameLasso# Orskippy-ddiabetes-tlinear_model-nLassowill run a linear regression with lasso regularization (L1)
on thediabetesdataset.Thedataargument can be any of
the following built-in scikit-learn datasets:RegressionbostondiabetesClassificationdigitsiriswinebreast_cancerThetypeandnamearguments are
referring to the model type and name from scikit-learn.
Thetypeis the submodule, e.g.linear_modelnaive_bayesensemblesvmwhile thenameis the what is actually imported, e.g.LinearRegressionGaussianNBRandomForestRegressorSVCSimplify code to a single function call per step:fromsklearn.metricsimportconfusion_matrix,accuracy_scoreimportskippyasskpdata=skp.get_data('digits')x_train,x_test,y_train,y_test=skp.split_data(data)model=skp.get_model(model_type='ensemble',model_name='RandomForestClassifier')fit=model.fit(x_train,y_train)skp.pickle_model(filename='digits_rf.pickle',model=fit)predictions=fit.predict(x_test)confmat=confusion_matrix(y_true=y_test,y_pred=predictions)accuracy=accuracy_score(y_true=y_test,y_pred=predictions)skp.confusion_matrix_plot(cm=confmat,acc=accuracy,filename='digits_rf.png')Or run a whole pipeline with one function:importskippyasskpskp.classification(dataset='digits',model_type='ensemble',model_name='RandomForestClassifier',pickle_name='digits_rf.pickle',plot_name='digits_rf.png')For inspiration, look at the example pipelines in thepipelines folderof theskippy repo.
Package: skippy-pad
Skippy - Simple. Comfortable. Powerful. notepad for www.scpwiki.comInstallationIf you have Python 3.7 or later you can install Skippy using pip:$ python -m pip install skippy-padAlso, if you haven't Python you can install compiled Skippy throughGithub releases.UsageIf you install Skippy through pip you can open console and write:$ python -m skippyFeaturesPowerful functionalFull previewUploading pages with title, source, tags and files in one clickDownloading pages with all dataEasy to use plugin systemSupport all wikidot sitesAutoupdating systemMulti language interfaceAdvanced autocomplete, suggestion and syntax engine to the Wikidot syntaxand many other features!ScreenshotsWhite themeBlack themeBugs/RequestsPlease use theGitHub issue trackerto submit bugs or request features.AuthorMrNereof- owner of Skippy.The Great Hippo- original creatorSkippy as a character.DonatingBuy me a coffee:Cryptocurrencies:Binance Smart Chain (BSC) - 0x581cc7acbe921b1fa9fccbbfe008c86f68bc097d (BTC, ETH, DOGE and etc.)SponsorsSkippy would like to thank the following organizations for graciously permitting us to use their services for free:JetBrains(info)LicenseThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program. If not, seehttps://www.gnu.org/licenses/gpl-3.0.txt.
Package: skip-ssg
SkipSkip is a Python-based data-forward static site generator inspired byEleventyWhy should I use Skip?You like Python and the Jinja2 templating engineYou want a simple static site generator that makes no assumptions about how your project should be structuredYou want to use data files, including dynamically computed data (like reading from a database or API)Quickstartpipinstallskip-ssgecho'# Hello World'>index.md
skipTo see more, check out thedocumentation
Package: skipthoughts
Skip-Thoughts.torch for PytorcbSkip-Thoughts.torchis a lightweight porting ofskip-thought pretrained models from Theanoto Pytorch.Installationpython3 with anacondapytorch with/out CUDAInstall from pippip install skipthoughtsInstall from repogit clone https://github.com/Cadene/skip-thoughts.torch.gitcd skip-thoughts.torch/pytorchpython setup.py installAvailable pretrained modelsUniSkipIt uses thenn.GRUlayer from torch with the cudnn backend. It is the fastest implementation, but the dropout is sampled after each time-step in the cudnn implementation... (equals bad regularization)DropUniSkipIt uses thenn.GRUCelllayer from torch with the cudnn backend. It is slightly slower than UniSkip, however the dropout is sampled once for all time-steps in a sequence (good regularization).BayesianUniSkipIt uses a custom GRU layer with a torch backend. It is at least two times slower than UniSkip, however the dropout is sampled once for all time-steps for each Linear (best regularization).BiSkipEquivalent to UniSkip, but with a bi-sequential GRU.Quick exampleimporttorchfromtorch.autogradimportVariableimportsyssys.path.append('skip-thoughts.torch/pytorch')fromskipthoughtsimportUniSkipdir_st='data/skip-thoughts'vocab=['robots','are','very','cool','<eos>','BiDiBu']uniskip=UniSkip(dir_st,vocab)input=Variable(torch.LongTensor([[1,2,3,4,0],# robots are very cool 0[6,2,3,4,5]# bidibu are very cool <eos>]))# <eos> token is optionalprint(input.size())# batch_size x seq_lenoutput_seq2vec=uniskip(input,lengths=[4,5])print(output_seq2vec.size())# batch_size x 2400output_seq2seq=uniskip(input)print(output_seq2seq.size())# batch_size x seq_len x 2400
Package: skipulagsstofnun
skipulagsstofnunLibrary to lookup polygons, coordinates and metadata of approved local site plans in Iceland.Used byPlanitor.Installbrewinstallgdal# or similarpoetryaddskipulagsstofnunorpipinstallskipulagsstofnunUse>>>fromskipulagsstofnunimportplans>>>shape,plan=plans.get_plan(64.1525571,-21.9508792)>>>plan{'id':'skipulag_deiliskipulag.198969','type':'Feature','skipnr':'8136','nrsveitarf':'0','sveitarfelag':'Reykjavíkurborg','heiti':'Deiliskipulag stgr. 1.116 og 1.115.3, Slippa- og Ellingsensreitur','skipstig':'deiliskipulag','malsmed':'nytt','dagsinnsett':None,'dagsleidrett':datetime.date(2016,4,14),'gagnaeigandi':'Skipulagsstofnun','dagsheimild':None,'heimild':None,'nakvaemnix':'0','vinnslufer':None}You can construct a link to a page hosted by Skipulagsstofnun with the PDF
scans for this local site plan.http://skipulagsaaetlanir.skipulagsstofnun.is/skipulagvefur/display.aspx?numer={skipnr}
Package: skiski
skiskiderive from Japanese word “好き好き(skiski)”. ThenskiskiLibrary is for peason who love SKI Combinator ;)PerposeI know that SKI Combinator influenced some esoteric language,UnlambdaandLazy K.but, it is too hard to use these language, because of strict about SKI Combinator, orFunctional Language.Then it is too hard to learn SKI Combinator.I want to getnot purely SKI Combinator Library, and decide DIY :).
Package: skit-game
SkitA tool for prototyping card games.Inspired bysquib, but I could never quite get my head around Ruby.InstallationPyPI didn't like the nameskit, so it's calledskit-game:pip install skit-game
# or
poetry add skit-gameBut the package inside is just calledskit.UsageSee theexamplesfor basic usage.fromskitimportDeckd=Deck(3)# make a deck of 3 cards, default sized.background('#cccccc')# set a gray backgroundd.render_png('basic_{index}.png')# render basic_0.png, basic_1.png, and basic_2.pngCreditsAndy Meneely for the idea, and for the awesome Squib!game-icons.net for icons used in the examples
Package: skittles
SkittlesWarning: 本项目仍处于开发阶段，API 可能会有较大变动。mirai-api-http Mock 测试工具。可以简单地将协议分为 事件（收到好友消息、收到群消息）和 命令（发送好友消息、发送群消息）两个部分。
对于客户端来说，做的事情是 ”接收事件、发送命令“，那么对于 服务端（Mock 工具）来说，做的事情就是 ”接收命令、发送事件“。实现的功能正向 Websocket用法pipinstallskittlesimportasyncioimporttimeimportloggingimporttypinglogging.basicConfig(level=logging.DEBUG)fromskittles.platformimportmiraifromskittles.entityimportbot,connectionbot_account=bot.Bot(account_id='12345678',nickname='bot',connection_types=[connection.ConnectionType.FORWARD_WS])# 建立假账号，目前仅支持 websocketmahadapter=mirai.MiraiAPIHTTPAdapter()@mahadapter.action_handler# 注册命令处理器asyncdefhandler(bot:bot.Bot,connection_type:connection.ConnectionType,data:dict)->typing.Any:print(bot,connection_type,data)asyncio.run(mahadapter.run(bots=[bot_account]))# 发送事件data={"type":"FriendMessage","sender":{"id":1010553892,"nickname":"Rock","remark":""},"messageChain":[{"type":"Source","id":123456,"time":int(time.time())},{"type":"Plain","text":"你好"},],}awaitmahadapter.emit_event(bots=[bot_account],event=data)awaitmahadapter.kill()# 断开连接、关闭服务
Package: skittle-sheets
Installpipinstallskittle-sheetsTo access private Google Sheets, generate a service account and JSON key file followinghttps://cloud.google.com/iam/docs/creating-managing-service-account-keysPlace the JSON key file in the working directory, a parent directory, your home directory, or~/config/skittle. Make sure the filename matches*service*.json(e.g,. move it to~/config/.skittle_service.json).Format and examplesThere are three meaningful blocks that are automatically extracted from the layout based on location. Any other content is ignored, so it's OK to put notes wherever you like.Key value pairs, likeexperiment: <experiment_name>. These must be at the top left of the sheet.Variable definitions. Each must be an Oklahoma-shaped region in the first three columns (variable name, value, and code).Grids mapping variables onto plates. The grid can be anywhere, but the spacing between variable name and row/column labels is important. Fill out the plate with codes (empty cells are OK). Plates labels can be listed after a semicolon (a grid with no plate label applies to all plates).Here are (some examples)[https://docs.google.com/spreadsheets/d/1UVDRgs1w6fySZqJqEbEH1v7m1i0qhhpQ_r9Y59TZ8tU/edit#gid=968744084]:# example Askittleexporthttps://docs.google.com/spreadsheets/d/1UVDRgs1w6fySZqJqEbEH1v7m1i0qhhpQ_r9Y59TZ8tU/edit#gid=968744084# example B (shown above)skittleexporthttps://docs.google.com/spreadsheets/d/1UVDRgs1w6fySZqJqEbEH1v7m1i0qhhpQ_r9Y59TZ8tU/edit#gid=767021684# example Cskittleexporthttps://docs.google.com/spreadsheets/d/1UVDRgs1w6fySZqJqEbEH1v7m1i0qhhpQ_r9Y59TZ8tU/edit#gid=1432994532UsagePublic Google Sheetsskittleexport<URL>Private Google SheetsShare your Google Sheets document with the service account generated above (read-only is fine). Then run:skittleexport"<spreadsheet>/<worksheet>"If the Drive and Sheets APIs are not already enabled, there will be an error message with a link prompting you to enable them.Local.csvfileskittleexportlayout.csv
Package: skkdictutils
# skkdictutils## Current statusThis library is under development.API is unstable.## Release processSeehttps://packaging.python.org/tutorials/packaging-projects/## LICENSECopyright (c) 2020 Tokuhiro MatsunoPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: skl2onnx
Introductionsklearn-onnxconvertsscikit-learnmodels toONNX.
Once in the ONNX format, you can use tools likeONNX Runtimefor high performance scoring.
All converters are tested withonnxruntime.
Any external converter can be registered to convert scikit-learn pipeline
including models or transformers coming from external libraries.DocumentationFull documentation including tutorials is available athttps://onnx.ai/sklearn-onnx/.Supported scikit-learn ModelsLast supported opset is 15.You may also find answers inexisting issuesor submit a new one.InstallationYou can install fromPyPi:pip install skl2onnxOr you can install from the source with the latest changes.pip install git+https://github.com/onnx/sklearn-onnx.gitGetting started# Train a model.importnumpyasnpfromsklearn.datasetsimportload_irisfromsklearn.model_selectionimporttrain_test_splitfromsklearn.ensembleimportRandomForestClassifieriris=load_iris()X,y=iris.data,iris.targetX=X.astype(np.float32)X_train,X_test,y_train,y_test=train_test_split(X,y)clr=RandomForestClassifier()clr.fit(X_train,y_train)# Convert into ONNX format.fromskl2onnximportto_onnxonx=to_onnx(clr,X[:1])withopen("rf_iris.onnx","wb")asf:f.write(onx.SerializeToString())# Compute the prediction with onnxruntime.importonnxruntimeasrtsess=rt.InferenceSession("rf_iris.onnx",providers=["CPUExecutionProvider"])input_name=sess.get_inputs()[0].namelabel_name=sess.get_outputs()[0].namepred_onx=sess.run([label_name],{input_name:X_test.astype(np.float32)})[0]ContributeWe welcome contributions in the form of feedback, ideas, or code.LicenseApache License v2.0
Package: sklad
a tiny cut from gp.fileupload-1.1, doing the actual job, with fixes and extensions.as HTML form, or jquery with multiple progress-per-upload;as single file and multiple file selections (HTML5)ok with UTF8 or cp1251 encoded non-ascii filenamesok with any file-sizes - doesn’t store anything in memoryuses os.path.getsize() to measure actual progress. hacks cgi.FieldStorage to handle tiny files (<1000 bytes)usage:in sklad.wsgi, application(), change the head/tail for needed html (default is both form and jquery).change FieldStorage.PATH to where files should be stored (relative)the relevant client file is jquery.fileupload.js, to change progress-representation.tweak .htaccess or in other way make sklad.wsgi accessible to apache (AddHandler wsgi-script .wsgi)for logging, uncomment fdebug and may change tpathhack it all as u likenotes for python version 2.5 and below:standard-lib cgi.py does not work - use the hacked/backported 25/cgi.py (put in same dir or fix imports in sklad.wsgi)there’s no json module in standard-lib; install simple_json insteadhave fun
www.svilendobrev.com
Package: sklearn
⚠️⚠️⚠️ Summary ⚠️⚠️⚠️⚠️⚠️⚠️ ThesklearnPyPI package is deprecated usescikit-learninstead⚠️⚠️⚠️How to fix the error for the main use casesusepip install scikit-learnrather thanpip install sklearnreplacesklearnbyscikit-learnin your pip requirements files
(requirements.txt,setup.py,setup.cfg,Pipfile, etc ...)if thesklearnpackage is used by one of your dependencies
it would be great if you take some time to track which package usessklearninstead ofscikit-learnand report it to their issue trackeras a last resort, set the environment variableSKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=Trueto avoid this errorReason for the deprecationsklearnpackage on PyPI exists to prevent malicious actors from using thesklearnpackage, sincesklearn(the import name) andscikit-learn(the
project name) are sometimes used interchangeably.scikit-learnis the actual
package name and should be used with pip, e.g. for:pip commands:pip install scikit-learnpip requirement files (requirements.txt,setup.py,setup.cfg,Pipfile, etc ...)At the time of writing (October 2022)sklearndownloads is about 1/5 of thescikit-learndownloads on PyPI so a lot of people are using it.There are some edge cases with the way the PyPIsklearnpackage is
implemented:pip install sklearn==1.1.3will say that the 1.1.3 version does not exist,
which is confusing. The only available version at the time of writing ofsklearnis 0.0.pip uninstall sklearnwill actually not uninstallscikit-learn, you can
still doimport sklearnafterwardsit can be confusing to have bothsklearnandscikit-learnin thepip listoutput, prompting questions like "why do I have scikit-learn 1.1.3 and
sklearn 0.0, and what does it even mean"?Historical brownout scheduleStarting 2023 December 1st, trying to install thesklearnPyPI package will
raise an error.The following table shows the historical brownout schedule that was used
between 2022 December 1st and 2023 December 1st, in order to get more people
aware of the deprecation.DatesWindow(s)2022 December 1st - 2023 January 31st:00-:05 every hour2023 February 1st - March 31st:00-:10 every hour2023 April 1st - May 31st:00-:15 every hour2023 June 1st - July 31st:00-:10 and :30-:40 every hour2023 August 1st - September 30th:00-:15 and :30-:45 every hour2023 October 1st - November 30th:00-:20 and :30-:50 every hour2023 December 1st onwardsalways raise an exception
Package: sklearn2
# sklearn2Sklearn2 is a small extension of scikit-learn.It is inspired by scikit-learn in order to be mergeable with it eventually.It is in development phase : it will change radically, often and without notice.## RequirementsSee requirements_conda.txt and requirements_pip.txt## Installation with conda$git clone https://github.com/vanoudh/sklearn2.git$cd sklearn2$conda install --file requirements_conda.txt$pip install .## Installation with pip$git clone https://github.com/vanoudh/sklearn2.git$cd sklearn2$pip install .## Test$pytest -v## UsageTODO
Package: sklearn2excel
sklearn2excelBringing Scikit-learn decision trees to ExcelWith this Python package, one can make a trained machine learning model
accessible to others without having to deploy it as a service.
More specifically, one can export a Scikit-learn decision
tree or random forest model to a Excel workbook.
All decision chains in the model will be represented within a single
table and feature values can be tested for an average prediction.Project overviewVersion: 0.1.1package levelexport_to_xlsx() (main access point)export_to_textfile() (alternative use)detects maximum tree depth and applies this parameterhelpers modulecreate_xlfile (project internal)writes a DecisionTreeTable object to a Excel sheetwrites features and an initial value of 1 to front sheetwrites decision trees to 2nd sheetcore moduleclass DecisionTreeTable (project internal)a class that can be instantiated with a parsed text filetransforms and represent decisions trees in a datastructureexposed properties to access info about the structureexposed methods to get tests and results as indexed rowshandle classifier- and regressor-type decision treesTODO:thoroughly testing (75%)Installationpipinstallsklearn2excelInstallation will install scikit-learn and XlsxWriter as well.Usage examplefrompathlibimportPathfromsklearn.ensembleimportRandomForestClassifierfromsklearn.preprocessingimportLabelEncoderimportsklear2excelass2e# fetch Scikit-learn wine example data as# sklearn.utils.Bunch object# and prepare example model from# sklearn.ensemble.RandomForestClassifier# RandomForestRegressor or any classifier/regressor# subtype of BaseDecisionTree could be usedbunch=s2e.get_data_target_and_features()wine_data=bunch.datawine_target=bunch.targetwine_features=bunch.feature_names[:4]X=wine_data[wine_features]y=LabelEncoder().fit_transform(wine_target)clf_model=RandomForestClassifier(n_estimators=10,min_samples_leaf=2).fit(X,y)path_xlsx=Path.cwd()/"excel_output.xlsx"path_txt=Path.cwd()/"text_output.txt"# export model as text file with use of# sklearn export function# first param single or ensemble of decision treess2e.export_to_textfile(clf_model.estimators_,# ensemble of decision treespath_txt,wine_features)# export model as Excel file# features written to Front sheet with initial value 1.0# decision trees written to 2nd sheets2e.export_to_xlsx(clf_model.estimators_,wine_features,path_xlsx)Development setupFlit ~3.4Release History0.1.1FIX: XlsxWriter dependency corrected0.1.0First proper releaseNEW: direct functionexport_to_xlsx()CHANGE: functions and class available at package-level0.0.1Work in progressMetaTorbjørn Wikestad –@TWikestad–torbjorn.wikestad@gmail.comDistributed under the MIT license. SeeLICENSEfor more information.https://github.com/tobisan5/github-linkContributingFork it (https://github.com/tobisan5/sklearn2excel/fork)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull Request
Package: sklearn2gem
# sklearn2gem[![Build Status](https://travis-ci.org/stewartpark/sklearn2gem.svg?branch=master)](https://travis-ci.org/stewartpark/sklearn2gem)
[![Requirements Status](https://requires.io/github/stewartpark/sklearn2gem/requirements.svg?branch=master)](https://requires.io/github/stewartpark/sklearn2gem/requirements/?branch=master)
[![PyPI version](https://badge.fury.io/py/sklearn2gem.svg)](https://badge.fury.io/py/sklearn2gem)⚡ sklearn2gem ports your scikit-learn model into a fast ruby C binding!# Getting startedInstall sklearn2gem usingpip:` pip install sklearn2gem `or viaeasy_install:` easy_install sklearn2gem `After that, dump your scikit-learn model withsklearn.externals.joblib, and runsklearn2gem model_name@version your_model.pkl foo/bar/model_name. You should be able to see a newly created folder namedmodel_nameunderfoo/bar/.See [examples/iris.py](https://github.com/stewartpark/sklearn2gem/blob/master/examples/iris.py) to try it out.To produce a pre-compiled binary gem, use [gem-compiler](https://github.com/luislavena/gem-compiler).# What machine learning algorithms are supported?Since sklearn2gem usesnok/sklearn-porterto convert a model into a C file, you can refer to [this page](https://github.com/nok/sklearn-porter#machine-learning-algorithms).