Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

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

Scikit-learn offers a user-friendly and effective suite of tools for predictive data analysis, making it an indispensable resource for those in the field. This powerful, open-source machine learning library is built for the Python programming language and aims to simplify the process of data analysis and modeling. Drawing from established scientific libraries like NumPy, SciPy, and Matplotlib, Scikit-learn presents a diverse array of both supervised and unsupervised learning algorithms, positioning itself as a crucial asset for data scientists, machine learning developers, and researchers alike. Its structure is designed to be both consistent and adaptable, allowing users to mix and match different components to meet their unique requirements. This modularity empowers users to create intricate workflows, streamline repetitive processes, and effectively incorporate Scikit-learn into expansive machine learning projects. Furthermore, the library prioritizes interoperability, ensuring seamless compatibility with other Python libraries, which greatly enhances data processing capabilities and overall efficiency. As a result, Scikit-learn stands out as a go-to toolkit for anyone looking to delve into the world of machine learning.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

.NET Yes 
Bing Yes 
C# Yes 
DagsHub No 
Databricks No 
GLM-5.1 No 
GLM-5.2 No 
GLM-5.3 No 
Google Cloud AutoML Yes 
Keepsake No 
MLJAR Studio No 
Matplotlib No 
Microsoft Defender Antivirus Yes 
Microsoft Outlook Yes 
Microsoft Power BI Yes 
ModelOp No 
NumPy No 
ONNX Yes 
Train in Data No 

Integrations

.NET No 
Bing No 
C# No 
DagsHub Yes 
Databricks Yes 
GLM-5.1 Yes 
GLM-5.2 Yes 
GLM-5.3 Yes 
Google Cloud AutoML No 
Keepsake Yes 
MLJAR Studio Yes 
Matplotlib Yes 
Microsoft Defender Antivirus No 
Microsoft Outlook No 
Microsoft Power BI No 
ModelOp Yes 
NumPy Yes 
ONNX No 
Train in Data Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Microsoft

Founded

1975

Country

United States

Website

dotnet.microsoft.com/en-us/apps/ai/ml-dotnet

Vendor Details

Company Name

scikit-learn

Country

United States

Website

scikit-learn.org/stable/

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Alternatives

Alternatives

ML.NET Reviews

ML.NET

Microsoft
Gensim Reviews

Gensim

Radim Řehůřek
AWS Neuron Reviews

AWS Neuron

Amazon Web Services
MLlib Reviews

MLlib

Apache Software Foundation
Apache Mahout Reviews

Apache Mahout

Apache Software Foundation