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Scikit-learn

General Information

Scikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language.

Scikit-learn

https://en.wikipedia.org/wiki/Scikit-learn

Scikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting...

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scikit-learn: machine learning in Python — scikit-learn...

https://scikit-learn.org/stable/index.html

"scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved "It allows us to do AWesome stuff we would not otherwise accomplish". "scikit-learn makes doing...

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scikit-learn: machine learning in Python — scikit-learn...

https://www.sklearn.org/

"scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved "For these tasks, we relied on the excellent scikit-learn package for Python." "The great benefit of...

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GitHub - scikit-learn/scikit-learn: scikit-learn: machine learning in...

https://github.com/scikit-learn/scikit-learn

scikit-learn: machine learning in Python. Contribute to scikit-learn/scikit-learn development by creating an account on GitHub.

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scikit-learn (@scikit_learn) | Твиттер

https://twitter.com/scikit_learn

scikit-learn 0.23.2 is out on PyPI and (soon) on conda-forge! pip install -U scikit-learn conda install -c conda-forge scikit-learn This release includes 14 bugfixes: https...

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Scikit-Learn Tutorial: Machine Learning in Python Examples

https://www.guru99.com/scikit-learn-tutorial.html

Scikit-learn is an open source Python library for machine learning. Scikit-learn is not very difficult to use and provides excellent results. However, scikit learn does not support parallel computations.

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Scikit-Learn Tutorial | Machine Learning With Scikit-Learn | Sklearn

https://www.youtube.com/watch?v=0Lt9w-BxKFQ

This Scikit-learn tutorial will help you understand what is Scikit-learn, what can we achieve using Scikit-learn and a demo on how to use Scikit-learn in...

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scikit-learn · PyPI

https://pypi.org/project/scikit-learn/

Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. scikit-learn 0.23 and later Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with "Display"...

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Scikit-learn Tutorial: Machine Learning in Python - Dataquest

https://www.dataquest.io/blog/sci-kit-learn-tutorial/

Scikit-learn is a free machine learning library for Python. It features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports Python numerical and...

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A Complete Python Scikit Learn Tutorial

https://www.c-sharpcorner.com/article/a-complete-scikit-learn-tutorial/

Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms...

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Introducing Scikit-Learn | Python Data Science Handbook

https://jakevdp.github.io/PythonDataSc[...]ok/05.02-introducing-scikit-learn.html

Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and We will start by covering data representation in Scikit-Learn, followed by covering the Estimator API...

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Installing scikit-learn — scikit-learn .17.dev0 documentation

http://scikit-learn.sourceforge.net/dev/install.html

Scikit-learn and its dependencies are all available as wheel packages for OSX Installing from source requires you to have installed the scikit-learn runtime dependencies, Python development...

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Python SciKit Learn Tutorial - JournalDev

https://www.journaldev.com/18341/python-scikit-learn-tutorial

Scikit-learn is a machine learning library for Python. It features several regression, classification and clustering algorithms including SVMs, gradient boosting, k-means, random forests and DBSCAN.

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scikit-learn documentation — DevDocs

https://devdocs.io/scikit_learn/

scikit-learn 0.20.0 API documentation with instant search, offline support, keyboard shortcuts The definitive description of key concepts and API elements for using scikit-learn and developing...

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SKLearn | Scikit-Learn In Python | SciKit Learn Tutorial

https://www.analyticsvidhya.com/blog/2[...]it-learn-python-machine-learning-tool/

Scikit-learn is a powerful Python library for machine learning & predictive modeling. This scikit learn tutorial gives an overview of scikit learn in python.

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Scikit Learn - Machine Learning using Python - Edureka

https://www.edureka.co/blog/scikit-learn-machine-learning/

Scikit learn blog will introduce you to Machine Learning in python. It includes a use case where we will implement logistic regression using scikit learn.

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Python Scikit Learn Example For Beginners

https://appdividend.com/2019/02/01/pyt[...]n-tutorial-for-beginners-with-example/

Scikit-learn is used to build the Machine Learning models, and it is not recommended to use it for reading, manipulating, and summarizing data as there are better frameworks available for the purpose...

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sklearn_extra.cluster.KMedoids — scikit-learn-extra...

https://scikit-learn-extra.readthedocs[...]ed/sklearn_extra.cluster.KMedoids.html

Sklearn_extra.cluster.KMedoids¶. Class sklearn_extra.cluster.KMedoids(n_clusters=8, metric='euclidean', init='heuristic', max_iter=300, random_state=None)[source] ¶. K-medoids clustering.

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Python Machine Learning: Scikit-Learn Tutorial - DataCamp

https://www.datacamp.com/community/tutorials/machine-learning-python

Typical tasks are concept learning, function learning or "predictive modeling", clustering and finding predictive Today's scikit-learn tutorial will introduce you to the basics of Python machine learning

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python - Difference between scikit-learn and sklearn - Stack Overflow

https://stackoverflow.com/questions/38[...]rence-between-scikit-learn-and-sklearn

What is the difference between scikit-learn and sklearn? Also, I cant import scikit-learn because of Also, try running the standard tests in scikit-learn and check the output. You will have detailed error...

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Learning Model Building in Scikit-learn : A Python... - GeeksforGeeks

https://www.geeksforgeeks.org/learning[...]learn-python-machine-learning-library/

Scikit-learn provides a wide range of machine learning algorithms which have a unified/consistent interface for fitting, predicting accuracy, etc. The example given below uses KNN...

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Scikit-Learn Iris

http://dx.doi.org/10.5281/zenodo.1404173

Data Set Characteristics: Number of Instances: 150 (50 in each of three classes) Number of Attributes: 4 numeric, predictive attributes and the class Attribute Information: sepal length in cm sepal width in cm petal length in cm petal width in cm class: Iris-Setosa Iris-Versicolour Iris-Virginica

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Scikit-Learn Regression Tuning

http:///10.1007/978-1-4842-5373-1_7

David Paper, 2020, 'Scikit-Learn Regression Tuning', Hands-on Scikit-Learn for Machine Learning Applications, pp. 189-213

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Introduction to Scikit-learn

http://link.springer.com/content/pdf/10.1007/978-1-4842-4470-8_18

Scikit-learn is a Python library that provides a standard interface for implementing machine learning algorithms. It includes other ancillary functions that are integral to the machine learning pipeline such as data preprocessing steps, data resampling techniques, evaluation parameters, and search interfaces for tuning/optimizing an algorithm’s performance.

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Introduction to Scikit-Learn

http:///10.1007/978-1-4842-5373-1_1

David Paper, 2020, 'Introduction to Scikit-Learn', Hands-on Scikit-Learn for Machine Learning Applications, pp. 1-35

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Machine Learning with scikit-learn

http://link.springer.com/content/pdf/10.1007/978-1-4842-0958-5_8

In the chain of processes that make up the data analysis, the construction phase of predictive models and their validation are done by a powerful library called scikit-learn. In this chapter you will see some examples that will illustrate the basic construction of predictive models with some different methods.

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Scikit-Learn Classifier Tuning from Simple Training Sets

http:///10.1007/978-1-4842-5373-1_5

David Paper, 2020, 'Scikit-Learn Classifier Tuning from Simple Training Sets', Hands-on Scikit-Learn for Machine Learning Applications, pp. 137-163

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Machine Learning for Neuroimaging with Scikit-Learn

http://arxiv.org/abs/1412.3919

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

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Scikit-Learn Classifier Tuning from Complex Training Sets

http:///10.1007/978-1-4842-5373-1_6

David Paper, 2020, 'Scikit-Learn Classifier Tuning from Complex Training Sets', Hands-on Scikit-Learn for Machine Learning Applications, pp. 165-188

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Scikit-learn: Machine Learning in Python

https://hal.inria.fr/hal-00650905

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.org.

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Getting Started with Scikit-learn for Machine Learning

http:///10.1002/9781119557500.ch5

2019, 'Getting Started with Scikit-learn for Machine Learning', Python® Machine Learning, pp. 93-117

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Scikit-Learn Regression Tuning

http://dx.doi.org/10.1007/978-1-4842-5373-1_7

Regression predictive modeling (or just regression) is the problem of learning the strength of association between independent variables (or features) and continuous dependent variables (or outcomes). Tuning regression algorithms is similar to tuning classification algorithms. That is, we adjust a model’s hyperparameters until we arrive at an optimal solution.

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Machine Learning with scikit-learn

http:///10.1007/978-1-4842-0958-5_8

Fabio Nelli, 2015, 'Machine Learning with scikit-learn', Python Data Analytics, pp. 237-264

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Scikit-learn

http:///10.1007/978-1-4842-4113-4_11

Valentina Porcu, 2018, 'Scikit-learn', Python for Data Mining Quick Syntax Reference, pp. 235-253

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Scikit-Learn: Machine Learning in the Python ecosystem

https://orbi.uliege.be/handle/2268/157487

The scikit-learn project is an increasingly popular machine learning library written in Python. It is designed to be simple and efficient, useful to both experts and non-experts, and reusable in a variety of contexts. The primary aim of the project is to provide a compendium of efficient implementations of classic, well-established machine learning algorithms. Among other things, it includes classical supervised and unsupervised learning algorithms, tools for model evaluation and selection, as well as tools for data preprocessing and feature engineering. This presentation will illustrate the use of scikit-learn as a component of the larger scientific Python environment to solve complex data analysis tasks. Examples will include end-to-end workflows based on powerful and popular algorithms in the library. Among others, we will show how to use out-of-core learning with on-the-fly feature extraction to tackle very large natural language processing tasks, how to exploit an IPython cluster for distributed cross-validation, or how to build and use random forests to explore biological data. Peer reviewed

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Hands-on Scikit-Learn for Machine Learning Applications

http:///10.1007/978-1-4842-5373-1

David Paper, 2020, 'Hands-on Scikit-Learn for Machine Learning Applications'

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Dataflow Acceleration of scikit-learn Gaussian Process Regression

https://zenodo.org/record/1406381

Big data revolution has sparked the widespread use of predictive data analytics based on sophisticated machine learning tasks. Fast data analysis have become very important, and this fact stresses software developers and computer architects to deliver more efficient design solutions able to address the in- creased performance requirements. Dataflow computing engines from Maxeler has been recently emerged as a promising way of performing high performance computation, utilizing FPGA devices. In this paper, we focus on exploiting Maxeler’s dataflow computing for accelerating Gaussian Process Regression from scikit-learn Python library, one of the most computationally intensive and with poor scaling characteistics machine learning algorithm. Through extensive analysis over diverse datasets, we point out which NumPy and SciPy functions forms the major performance bottlenecks that should be implemented in a dataflow acceleration engine and then we discuss the mapping decisions that enable the generation of parameterized dataflow engines. Finally, we show that the proposed acceleration solution delivers significant speedups for the examined datasets, while it also reports good scalability in respect to increased dataset sizes.

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A scikit-learn compatible learning classifier system

http:///10.1145/3377929.3398097

Robert F. Zhang,Ryan J. Urbanowicz, 2020, 'A scikit-learn compatible learning classifier system', Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

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Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn

http:///10.25080/majora-14bd3278-006

Brent Komer,James Bergstra,Chris Eliasmith, 2014, 'Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn', Proceedings of the 13th Python in Science Conference

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Gesture Keyboard For Universal Input

https://hackaday.com/2017/11/29/gesture-keyboard-for-universal-input/

Each letter is individually learned by his Python code and scikit-learn’s Support Vector Machine. There’s nothing holding a user back from giving single-letter commands to your favorite programs.

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Last Minute Deal: Save 96% on the Complete Python eBook & Video Course Bundle

https://www.geeky-gadgets.com/last-min[...]-ebook-video-course-bundle-26-08-2020/

Just a quick reminder four our readers about the great deal on the Complete Python eBook & Video Course Bundle n the Geeky Gadgets Deals store this week. The Complete Python eBook & Video Course ...

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Tiny Machine Learning On The Attiny85

https://hackaday.com/2020/01/07/tiny-machine-learning-on-the-attiny85/

Using his experience of running ML models on an old Arduino Nano, he had created a generator that can export C code from a scikit-learn. He tried using this generator to compile a support-vector ...

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AI Governance Market Growing at a CAGR 44.3% | Key Player IBM, Google, Microsoft, Facebook, Salesforce.Com

https://www.marketwatch.com/press-rele[...]soft-facebook-salesforcecom-2020-08-21

In July 2019, the company added new capabilities to its What-If Tool to enable users to use this tool for XGBoost and Scikit-learn models that are deployed on the AI platform. The user can enable ...

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Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing

https://www.nature.com/articles/s41562-020-00944-2

How We Feel is a web and mobile-phone application for collecting de-identified self-reported COVID-19-related data. These data are used to map a diverse set of symptomatic, demographic, exposure and ...

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AI Governance Market Growing at a CAGR 44.3% | Key Player IBM, Google, Microsoft, Facebook, Salesforce.Com

https://www.benzinga.com/pressreleases[...]google-microsoft-facebook-salesforce-c

The solution segment to hold the largest market size during the forecast period. The AI governance solution refers to the platform and software tools, which provides end-to-end AI ...

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Ebook Hands-On Machine Learning with Scikit-Learn and TensorFlow Full

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https://getonbook.tryin.space/?book=1491962291Full E-book Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how., By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you ve learned, all you need is programming experience to get started., Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details For Full

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https://newsteler45.blogspot.com/?book=1491962291Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how., By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you ve learned, all you need is programming experience to get started., Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details

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https://goodreadsb.blogspot.com/?book=1491962291Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how., By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you ve learned, all you need is programming experience to get started., Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details

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https://kpf.realfiedbook.com/?book=1491962291Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks--scikit-learn and TensorFlow--author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. * Explore the machine learning landscape, particularly neural nets * Use scikit-learn to track an example machine-learning project end-to-end * Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods * Use the TensorFlow library to build and train neural nets * Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning * Learn techniques for training and scaling deep neural nets * Apply practical code examples without acquiring excessive machine learning theory or algorithm details

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With Attributes

  • Creator: David Cournapeau
  • Developer: David Cournapeau
  • Copyright license: 3-clause BSD License
  • Programming language: Python, C, C++, Cython
  • Software version identifier: 0.23.2