VBScript

VBScript

Microsoft
+

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About

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

About

VBScript is a programming language included with Microsoft Internet Explorer. For other browsers, contact your vendor about support. VBScript 2.0 (or later) is recommended for use with Agent. Although earlier versions of VBScript may work with Agent, they lack certain functions that you may want to use. You can download VBScript 2.0 and obtain further information on VBScript at the Microsoft Downloads site and the Microsoft VBScript site. With VBScript (2.0 or later), you can verify whether Microsoft Agent is installed by trying to create the object and checking to see if it exists. The following sample demonstrates how to check for the Agent control without triggering an auto-download of the control.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Component Library solution for DevOps teams

Audience

Programming Language solution for developers

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

NumPy
numpy.org

Company Information

Microsoft
docs.microsoft.com/en-us/windows/win32/lwef/using-vbscript

Company Information

scikit-learn
United States
scikit-learn.org/stable/

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Replicate

Categories

Categories

Categories

Integrations

Codédex
Cython
DagsHub
Dash
Flower
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Matplotlib
ModelOp
NVIDIA FLARE
PaizaCloud
PyCharm
SlickEdit
SmartBear AQTime Pro
Spyder
Typora
Yandex Data Proc
scikit-learn

Integrations

Codédex
Cython
DagsHub
Dash
Flower
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Matplotlib
ModelOp
NVIDIA FLARE
PaizaCloud
PyCharm
SlickEdit
SmartBear AQTime Pro
Spyder
Typora
Yandex Data Proc
scikit-learn

Integrations

Codédex
Cython
DagsHub
Dash
Flower
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Matplotlib
ModelOp
NVIDIA FLARE
PaizaCloud
PyCharm
SlickEdit
SmartBear AQTime Pro
Spyder
Typora
Yandex Data Proc
scikit-learn
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