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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

Pandas is an open-source data analysis and manipulation tool that is not only fast and powerful but also highly flexible and user-friendly, all within the Python programming ecosystem. It provides various tools for importing and exporting data across different formats, including CSV, text files, Microsoft Excel, SQL databases, and the efficient HDF5 format. With its intelligent data alignment capabilities and integrated management of missing values, users benefit from automatic label-based alignment during computations, which simplifies the process of organizing disordered data. The library features a robust group-by engine that allows for sophisticated aggregating and transforming operations, enabling users to easily perform split-apply-combine actions on their datasets. Additionally, pandas offers extensive time series functionality, including the ability to generate date ranges, convert frequencies, and apply moving window statistics, as well as manage date shifting and lagging. Users can even create custom time offsets tailored to specific domains and join time series data without the risk of losing any information. This comprehensive set of features makes pandas an essential tool for anyone working with data in Python.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

.NET
3LC
Activeeon ProActive
Amazon SageMaker Data Wrangler
ApertureDB
Cleanlab
Coiled
Dagster
Dash
Flower
Giskard
Google Cloud AutoML
LanceDB
Microsoft Defender Antivirus
Microsoft Outlook
Netdata
RunCode
TensorFlow
ThinkData Works
Yandex Data Proc

Integrations

.NET
3LC
Activeeon ProActive
Amazon SageMaker Data Wrangler
ApertureDB
Cleanlab
Coiled
Dagster
Dash
Flower
Giskard
Google Cloud AutoML
LanceDB
Microsoft Defender Antivirus
Microsoft Outlook
Netdata
RunCode
TensorFlow
ThinkData Works
Yandex Data Proc

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

pandas

Founded

2008

Website

pandas.pydata.org

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Product Features

Data Analysis

Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics

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