Best Machine Learning Software - Page 3

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Researched and written by Matthew Miller

Machine learning software automates tasks for users by leveraging an algorithm to produce an output. These solutions are typically embedded into various platforms and have use cases across a wide variety of industries. Machine learning solutions improve the speed and accuracy of desired outputs by constantly refining them as the application digests more training data. Machine learning software improves processes and introduces efficiency to multiple industries, ranging from financial services to agriculture. Machine learning applications include process automation, customer service, security risk identification, and contextual collaboration.

Notably, end users of machine learning-powered applications do not interact with the algorithm directly. Rather, machine learning powers the backend of the artificial intelligence (AI) that users interact with. Some prime examples of this include chatbots software and automated insurance claims management software

To qualify for inclusion in the Machine Learning category, a product must:

Offer an algorithm or product that learns and adapts based on data
Be the source of intelligent learning capabilities for applications
Consume data inputs from a variety of data pools
Provide an output that solves a specific issue based on the learned data

Best Machine Learning Software At A Glance

Leader:
Highest Performer:
Best Contender:
Most Trending:
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Most Trending:

G2 takes pride in showing unbiased reviews on user satisfaction in our ratings and reports. We do not allow paid placements in any of our ratings, rankings, or reports. Learn about our scoring methodologies.

No filters applied
226 Listings in Machine Learning Available
(13)4.4 out of 5
8th Easiest To Use in Machine Learning software
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    XGBoost is an optimized distributed gradient boosting library that is efficient, flexible and portable, it implements machine learning algorithms under the Gradient Boosting framework and provides a p

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 46% Small-Business
    • 31% Enterprise
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • XGBoost features and usability ratings that predict user satisfaction
    8.3
    Has the product been a good partner in doing business?
    Average: 8.8
    8.9
    Ease of Use
    Average: 8.4
    7.6
    Quality of Support
    Average: 8.4
    8.3
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    XGBoost
    Year Founded
    2008
    HQ Location
    San Francisco, US
    Twitter
    @github
    2,622,895 Twitter followers
Product Description
How are these determined?Information
This description is provided by the seller.

XGBoost is an optimized distributed gradient boosting library that is efficient, flexible and portable, it implements machine learning algorithms under the Gradient Boosting framework and provides a p

Users
No information available
Industries
No information available
Market Segment
  • 46% Small-Business
  • 31% Enterprise
XGBoost features and usability ratings that predict user satisfaction
8.3
Has the product been a good partner in doing business?
Average: 8.8
8.9
Ease of Use
Average: 8.4
7.6
Quality of Support
Average: 8.4
8.3
Ease of Admin
Average: 8.6
Seller Details
Seller
XGBoost
Year Founded
2008
HQ Location
San Francisco, US
Twitter
@github
2,622,895 Twitter followers
(53)4.8 out of 5
View top Consulting Services for V7
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Entry Level Price:Free
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    V7 is a powerful AI training data platform that enables you to annotate images, videos, documents, and medical imaging files. It is the quickest way to obtain high-quality annotated data for training

    Users
    No information available
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 55% Small-Business
    • 36% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • V7 Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    7
    Annotation Efficiency
    4
    Annotation Tools
    4
    Intuitive
    4
    Efficiency
    3
    Cons
    Lacking Features
    4
    Missing Features
    4
    Annotation Issues
    2
    Limited Features
    2
    Data Limitations
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • V7 features and usability ratings that predict user satisfaction
    9.9
    Has the product been a good partner in doing business?
    Average: 8.8
    9.6
    Ease of Use
    Average: 8.4
    9.6
    Quality of Support
    Average: 8.4
    9.4
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    V7
    Year Founded
    2018
    HQ Location
    London, England
    Twitter
    @v7labs
    3,322 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    87 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

V7 is a powerful AI training data platform that enables you to annotate images, videos, documents, and medical imaging files. It is the quickest way to obtain high-quality annotated data for training

Users
No information available
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 55% Small-Business
  • 36% Mid-Market
V7 Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
7
Annotation Efficiency
4
Annotation Tools
4
Intuitive
4
Efficiency
3
Cons
Lacking Features
4
Missing Features
4
Annotation Issues
2
Limited Features
2
Data Limitations
1
V7 features and usability ratings that predict user satisfaction
9.9
Has the product been a good partner in doing business?
Average: 8.8
9.6
Ease of Use
Average: 8.4
9.6
Quality of Support
Average: 8.4
9.4
Ease of Admin
Average: 8.6
Seller Details
Seller
V7
Year Founded
2018
HQ Location
London, England
Twitter
@v7labs
3,322 Twitter followers
LinkedIn® Page
www.linkedin.com
87 employees on LinkedIn®

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  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Conjecture is a framework for building machine learning models in Hadoop using the Scalding DSL that enable the development of statistical models as viable components in a wide range of product settin

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 64% Small-Business
    • 18% Mid-Market
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Conjecture features and usability ratings that predict user satisfaction
    6.7
    Has the product been a good partner in doing business?
    Average: 8.8
    8.1
    Ease of Use
    Average: 8.4
    8.8
    Quality of Support
    Average: 8.4
    8.3
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Year Founded
    2018
    HQ Location
    Perth, Australia
    LinkedIn® Page
    www.linkedin.com
Product Description
How are these determined?Information
This description is provided by the seller.

Conjecture is a framework for building machine learning models in Hadoop using the Scalding DSL that enable the development of statistical models as viable components in a wide range of product settin

Users
No information available
Industries
No information available
Market Segment
  • 64% Small-Business
  • 18% Mid-Market
Conjecture features and usability ratings that predict user satisfaction
6.7
Has the product been a good partner in doing business?
Average: 8.8
8.1
Ease of Use
Average: 8.4
8.8
Quality of Support
Average: 8.4
8.3
Ease of Admin
Average: 8.6
Seller Details
Year Founded
2018
HQ Location
Perth, Australia
LinkedIn® Page
www.linkedin.com
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Apache PredictionIO is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learn

    Users
    No information available
    Industries
    • Computer Software
    Market Segment
    • 39% Small-Business
    • 33% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Apache PredictionIO Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Deployment Ease
    1
    Ease of Use
    1
    Implementation Ease
    1
    Integrations
    1
    Quality
    1
    Cons
    Limited Features
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Apache PredictionIO features and usability ratings that predict user satisfaction
    10.0
    Has the product been a good partner in doing business?
    Average: 8.8
    8.8
    Ease of Use
    Average: 8.4
    8.4
    Quality of Support
    Average: 8.4
    10.0
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Year Founded
    1999
    HQ Location
    Wakefield, MA
    Twitter
    @TheASF
    66,234 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    2,291 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Apache PredictionIO is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learn

Users
No information available
Industries
  • Computer Software
Market Segment
  • 39% Small-Business
  • 33% Mid-Market
Apache PredictionIO Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Deployment Ease
1
Ease of Use
1
Implementation Ease
1
Integrations
1
Quality
1
Cons
Limited Features
1
Apache PredictionIO features and usability ratings that predict user satisfaction
10.0
Has the product been a good partner in doing business?
Average: 8.8
8.8
Ease of Use
Average: 8.4
8.4
Quality of Support
Average: 8.4
10.0
Ease of Admin
Average: 8.6
Seller Details
Year Founded
1999
HQ Location
Wakefield, MA
Twitter
@TheASF
66,234 Twitter followers
LinkedIn® Page
www.linkedin.com
2,291 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    go-galib is a genetic algorithms for Go/Golang

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 50% Small-Business
    • 43% Enterprise
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Genetic Algorithms for Go/Golang features and usability ratings that predict user satisfaction
    9.2
    Has the product been a good partner in doing business?
    Average: 8.8
    8.1
    Ease of Use
    Average: 8.4
    7.6
    Quality of Support
    Average: 8.4
    7.5
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    HQ Location
    N/A
Product Description
How are these determined?Information
This description is provided by the seller.

go-galib is a genetic algorithms for Go/Golang

Users
No information available
Industries
No information available
Market Segment
  • 50% Small-Business
  • 43% Enterprise
Genetic Algorithms for Go/Golang features and usability ratings that predict user satisfaction
9.2
Has the product been a good partner in doing business?
Average: 8.8
8.1
Ease of Use
Average: 8.4
7.6
Quality of Support
Average: 8.4
7.5
Ease of Admin
Average: 8.6
Seller Details
HQ Location
N/A
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    zAdviser uses machine learning to find correlations between developer behaviors and key performance indicators (KPIs) based on DevOps data and Compuware product usage data. zAdviser captures a broa

    Users
    No information available
    Industries
    • Information Technology and Services
    Market Segment
    • 50% Enterprise
    • 30% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • BMC Compuware zAdviser Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    3
    Customer Support
    2
    Machine Learning
    2
    Analytics
    1
    Easy Setup
    1
    Cons
    Complexity
    1
    Limited Customization
    1
    Not Intuitive
    1
    Poor Interface Design
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • BMC Compuware zAdviser features and usability ratings that predict user satisfaction
    7.2
    Has the product been a good partner in doing business?
    Average: 8.8
    8.6
    Ease of Use
    Average: 8.4
    8.6
    Quality of Support
    Average: 8.4
    8.3
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Year Founded
    1980
    HQ Location
    Houston, TX
    Twitter
    @BMCSoftware
    49,856 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    9,879 employees on LinkedIn®
    Phone
    713 918 8800
Product Description
How are these determined?Information
This description is provided by the seller.

zAdviser uses machine learning to find correlations between developer behaviors and key performance indicators (KPIs) based on DevOps data and Compuware product usage data. zAdviser captures a broa

Users
No information available
Industries
  • Information Technology and Services
Market Segment
  • 50% Enterprise
  • 30% Small-Business
BMC Compuware zAdviser Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
3
Customer Support
2
Machine Learning
2
Analytics
1
Easy Setup
1
Cons
Complexity
1
Limited Customization
1
Not Intuitive
1
Poor Interface Design
1
BMC Compuware zAdviser features and usability ratings that predict user satisfaction
7.2
Has the product been a good partner in doing business?
Average: 8.8
8.6
Ease of Use
Average: 8.4
8.6
Quality of Support
Average: 8.4
8.3
Ease of Admin
Average: 8.6
Seller Details
Year Founded
1980
HQ Location
Houston, TX
Twitter
@BMCSoftware
49,856 Twitter followers
LinkedIn® Page
www.linkedin.com
9,879 employees on LinkedIn®
Phone
713 918 8800
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    SuperLearner is a package that implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 38% Small-Business
    • 31% Enterprise
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • SuperLearner features and usability ratings that predict user satisfaction
    8.3
    Has the product been a good partner in doing business?
    Average: 8.8
    9.3
    Ease of Use
    Average: 8.4
    8.5
    Quality of Support
    Average: 8.4
    5.0
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Year Founded
    2018
    HQ Location
    Miami, US
    LinkedIn® Page
    www.linkedin.com
    1,149 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

SuperLearner is a package that implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.

Users
No information available
Industries
No information available
Market Segment
  • 38% Small-Business
  • 31% Enterprise
SuperLearner features and usability ratings that predict user satisfaction
8.3
Has the product been a good partner in doing business?
Average: 8.8
9.3
Ease of Use
Average: 8.4
8.5
Quality of Support
Average: 8.4
5.0
Ease of Admin
Average: 8.6
Seller Details
Year Founded
2018
HQ Location
Miami, US
LinkedIn® Page
www.linkedin.com
1,149 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    MLlib is Spark's machine learning (ML) library that make practical machine learning scalable and easy it provides ML Algorithms: common learning algorithms such as classification, regression, clusteri

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 50% Mid-Market
    • 29% Enterprise
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • MLlib features and usability ratings that predict user satisfaction
    7.6
    Has the product been a good partner in doing business?
    Average: 8.8
    8.8
    Ease of Use
    Average: 8.4
    7.3
    Quality of Support
    Average: 8.4
    7.9
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Year Founded
    1999
    HQ Location
    Wakefield, MA
    Twitter
    @TheASF
    66,234 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    2,291 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

MLlib is Spark's machine learning (ML) library that make practical machine learning scalable and easy it provides ML Algorithms: common learning algorithms such as classification, regression, clusteri

Users
No information available
Industries
No information available
Market Segment
  • 50% Mid-Market
  • 29% Enterprise
MLlib features and usability ratings that predict user satisfaction
7.6
Has the product been a good partner in doing business?
Average: 8.8
8.8
Ease of Use
Average: 8.4
7.3
Quality of Support
Average: 8.4
7.9
Ease of Admin
Average: 8.6
Seller Details
Year Founded
1999
HQ Location
Wakefield, MA
Twitter
@TheASF
66,234 Twitter followers
LinkedIn® Page
www.linkedin.com
2,291 employees on LinkedIn®
(21)4.6 out of 5
View top Consulting Services for PyTorch
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Choose Your Path: Install PyTorch Locally or Launch Instantly on Supported Cloud Platforms

    Users
    No information available
    Industries
    • Computer Software
    Market Segment
    • 43% Small-Business
    • 38% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • PyTorch Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    6
    Machine Learning
    5
    Model Variety
    4
    Documentation
    3
    Quality
    3
    Cons
    Difficult Learning
    2
    Poor Documentation
    2
    Compatibility Issues
    1
    Inaccuracy
    1
    Lagging Issues
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • PyTorch features and usability ratings that predict user satisfaction
    8.3
    Has the product been a good partner in doing business?
    Average: 8.8
    8.6
    Ease of Use
    Average: 8.4
    7.9
    Quality of Support
    Average: 8.4
    8.3
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Jetware
    Year Founded
    2017
    HQ Location
    Roma, IT
    Twitter
    @jetware_io
    25 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    2 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Choose Your Path: Install PyTorch Locally or Launch Instantly on Supported Cloud Platforms

Users
No information available
Industries
  • Computer Software
Market Segment
  • 43% Small-Business
  • 38% Mid-Market
PyTorch Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
6
Machine Learning
5
Model Variety
4
Documentation
3
Quality
3
Cons
Difficult Learning
2
Poor Documentation
2
Compatibility Issues
1
Inaccuracy
1
Lagging Issues
1
PyTorch features and usability ratings that predict user satisfaction
8.3
Has the product been a good partner in doing business?
Average: 8.8
8.6
Ease of Use
Average: 8.4
7.9
Quality of Support
Average: 8.4
8.3
Ease of Admin
Average: 8.6
Seller Details
Seller
Jetware
Year Founded
2017
HQ Location
Roma, IT
Twitter
@jetware_io
25 Twitter followers
LinkedIn® Page
www.linkedin.com
2 employees on LinkedIn®
(10)4.6 out of 5
1st Easiest To Use in Machine Learning software
Save to My Lists
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Crab as known as scikits.recommender is a Python framework for building recommender engines that integrate with the world of scientific Python packages (numpy, scipy, matplotlib), provide a rich set o

    Users
    No information available
    Industries
    • Computer Software
    Market Segment
    • 50% Small-Business
    • 40% Mid-Market
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Crab features and usability ratings that predict user satisfaction
    9.5
    Has the product been a good partner in doing business?
    Average: 8.8
    8.7
    Ease of Use
    Average: 8.4
    9.3
    Quality of Support
    Average: 8.4
    9.2
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Crab
    Year Founded
    2012
    HQ Location
    N/A
    LinkedIn® Page
    www.linkedin.com
    22 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Crab as known as scikits.recommender is a Python framework for building recommender engines that integrate with the world of scientific Python packages (numpy, scipy, matplotlib), provide a rich set o

Users
No information available
Industries
  • Computer Software
Market Segment
  • 50% Small-Business
  • 40% Mid-Market
Crab features and usability ratings that predict user satisfaction
9.5
Has the product been a good partner in doing business?
Average: 8.8
8.7
Ease of Use
Average: 8.4
9.3
Quality of Support
Average: 8.4
9.2
Ease of Admin
Average: 8.6
Seller Details
Seller
Crab
Year Founded
2012
HQ Location
N/A
LinkedIn® Page
www.linkedin.com
22 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Milk is a machine learning toolkit in Python that focuses on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also perform

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 50% Mid-Market
    • 42% Small-Business
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • MILK features and usability ratings that predict user satisfaction
    7.8
    Has the product been a good partner in doing business?
    Average: 8.8
    7.3
    Ease of Use
    Average: 8.4
    7.0
    Quality of Support
    Average: 8.4
    7.8
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    MILK
    Year Founded
    2008
    HQ Location
    New York, NY
    Twitter
    @github
    2,622,895 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    5,749 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Milk is a machine learning toolkit in Python that focuses on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also perform

Users
No information available
Industries
No information available
Market Segment
  • 50% Mid-Market
  • 42% Small-Business
MILK features and usability ratings that predict user satisfaction
7.8
Has the product been a good partner in doing business?
Average: 8.8
7.3
Ease of Use
Average: 8.4
7.0
Quality of Support
Average: 8.4
7.8
Ease of Admin
Average: 8.6
Seller Details
Seller
MILK
Year Founded
2008
HQ Location
New York, NY
Twitter
@github
2,622,895 Twitter followers
LinkedIn® Page
www.linkedin.com
5,749 employees on LinkedIn®
(14)4.5 out of 5
15th Easiest To Use in Machine Learning software
Save to My Lists
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    python-recsys is a python library for implementing a recommender system.

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 50% Enterprise
    • 36% Small-Business
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • python-recsys features and usability ratings that predict user satisfaction
    9.4
    Has the product been a good partner in doing business?
    Average: 8.8
    8.3
    Ease of Use
    Average: 8.4
    8.7
    Quality of Support
    Average: 8.4
    9.2
    Ease of Admin
    Average: 8.6
  • Seller Details
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  • Seller Details
    Year Founded
    2003
    HQ Location
    N/A
    Twitter
    @ThePSF
    682,523 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    2 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

python-recsys is a python library for implementing a recommender system.

Users
No information available
Industries
No information available
Market Segment
  • 50% Enterprise
  • 36% Small-Business
python-recsys features and usability ratings that predict user satisfaction
9.4
Has the product been a good partner in doing business?
Average: 8.8
8.3
Ease of Use
Average: 8.4
8.7
Quality of Support
Average: 8.4
9.2
Ease of Admin
Average: 8.6
Seller Details
Year Founded
2003
HQ Location
N/A
Twitter
@ThePSF
682,523 Twitter followers
LinkedIn® Page
www.linkedin.com
2 employees on LinkedIn®
(15)4.4 out of 5
12th Easiest To Use in Machine Learning software
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.

    Users
    No information available
    Industries
    • Computer Software
    Market Segment
    • 40% Enterprise
    • 40% Small-Business
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Torch features and usability ratings that predict user satisfaction
    7.8
    Has the product been a good partner in doing business?
    Average: 8.8
    8.9
    Ease of Use
    Average: 8.4
    8.1
    Quality of Support
    Average: 8.4
    8.3
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Year Founded
    2017
    HQ Location
    San Francisco, US
    Twitter
    @torchlabs
    3,129 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    354 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.

Users
No information available
Industries
  • Computer Software
Market Segment
  • 40% Enterprise
  • 40% Small-Business
Torch features and usability ratings that predict user satisfaction
7.8
Has the product been a good partner in doing business?
Average: 8.8
8.9
Ease of Use
Average: 8.4
8.1
Quality of Support
Average: 8.4
8.3
Ease of Admin
Average: 8.6
Seller Details
Year Founded
2017
HQ Location
San Francisco, US
Twitter
@torchlabs
3,129 Twitter followers
LinkedIn® Page
www.linkedin.com
354 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    DecisionTree.jl is a Julia classifier with the implimentation of the ID3 algorithm with post pruning (pessimistic pruning), parallelized bagging (random forests), adaptive boosting (decision stumps),

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 36% Mid-Market
    • 36% Small-Business
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • DecisionTree.jl features and usability ratings that predict user satisfaction
    8.3
    Has the product been a good partner in doing business?
    Average: 8.8
    7.4
    Ease of Use
    Average: 8.4
    6.9
    Quality of Support
    Average: 8.4
    6.7
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    HQ Location
    N/A
Product Description
How are these determined?Information
This description is provided by the seller.

DecisionTree.jl is a Julia classifier with the implimentation of the ID3 algorithm with post pruning (pessimistic pruning), parallelized bagging (random forests), adaptive boosting (decision stumps),

Users
No information available
Industries
No information available
Market Segment
  • 36% Mid-Market
  • 36% Small-Business
DecisionTree.jl features and usability ratings that predict user satisfaction
8.3
Has the product been a good partner in doing business?
Average: 8.8
7.4
Ease of Use
Average: 8.4
6.9
Quality of Support
Average: 8.4
6.7
Ease of Admin
Average: 8.6
Seller Details
HQ Location
N/A
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    rapaio is a statistics, data mining and machine learning toolbox

    Users
    No information available
    Industries
    No information available
    Market Segment
    • 60% Small-Business
    • 40% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • rapaio Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    4
    Intuitive
    2
    Machine Learning
    2
    Analytics
    1
    Customer Support
    1
    Cons
    AI Limitations
    1
    Difficult Learning
    1
    Difficulty for Beginners
    1
    Limited Diversity
    1
    Missing Features
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • rapaio features and usability ratings that predict user satisfaction
    5.0
    Has the product been a good partner in doing business?
    Average: 8.8
    7.7
    Ease of Use
    Average: 8.4
    6.9
    Quality of Support
    Average: 8.4
    5.8
    Ease of Admin
    Average: 8.6
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    rapaio
    HQ Location
    N/A
Product Description
How are these determined?Information
This description is provided by the seller.

rapaio is a statistics, data mining and machine learning toolbox

Users
No information available
Industries
No information available
Market Segment
  • 60% Small-Business
  • 40% Mid-Market
rapaio Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
4
Intuitive
2
Machine Learning
2
Analytics
1
Customer Support
1
Cons
AI Limitations
1
Difficult Learning
1
Difficulty for Beginners
1
Limited Diversity
1
Missing Features
1
rapaio features and usability ratings that predict user satisfaction
5.0
Has the product been a good partner in doing business?
Average: 8.8
7.7
Ease of Use
Average: 8.4
6.9
Quality of Support
Average: 8.4
5.8
Ease of Admin
Average: 8.6
Seller Details
Seller
rapaio
HQ Location
N/A