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Description
ML.NET is a versatile, open-source machine learning framework that is free to use and compatible across platforms, enabling .NET developers to create tailored machine learning models using C# or F# while remaining within the .NET environment. This framework encompasses a wide range of machine learning tasks such as classification, regression, clustering, anomaly detection, and recommendation systems. Additionally, ML.NET seamlessly integrates with other renowned machine learning frameworks like TensorFlow and ONNX, which broadens the possibilities for tasks like image classification and object detection. It comes equipped with user-friendly tools such as Model Builder and the ML.NET CLI, leveraging Automated Machine Learning (AutoML) to streamline the process of developing, training, and deploying effective models. These innovative tools automatically analyze various algorithms and parameters to identify the most efficient model for specific use cases. Moreover, ML.NET empowers developers to harness the power of machine learning without requiring extensive expertise in the field.
Description
Weka comprises a suite of machine learning algorithms designed for various data mining activities. This platform offers functionalities for tasks such as data preparation, classification, regression, clustering, association rule mining, and data visualization. Interestingly, Weka is also the name of a flightless bird native to New Zealand, known for its curious disposition. The pronunciation of the name and the sounds made by the bird can be found online. As an open-source software, Weka is available under the GNU General Public License. We have created several complimentary online courses aimed at teaching machine learning and data mining through Weka, with video resources accessible on YouTube. The emergence and implementation of machine learning techniques represent a groundbreaking advancement in the realm of computer science. These techniques empower computer programs to systematically analyze extensive datasets and discern the most pertinent information. Consequently, this distilled knowledge can facilitate automated predictions and accelerate decision-making processes for individuals and organizations alike. This intersection of nature and technology showcases the fascinating ways in which we draw inspiration from the world around us.
API Access
Has API
API Access
Has API
Integrations
.NET
AWS Marketplace
Bing
C#
F#
Google Cloud AutoML
Microsoft Defender Antivirus
Microsoft Outlook
Microsoft Power BI
ONNX
Integrations
.NET
AWS Marketplace
Bing
C#
F#
Google Cloud AutoML
Microsoft Defender Antivirus
Microsoft Outlook
Microsoft Power BI
ONNX
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
dotnet.microsoft.com/en-us/apps/ai/ml-dotnet
Vendor Details
Company Name
University of Waikato
Country
New Zealand
Website
www.cs.waikato.ac.nz/ml/weka/
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization