EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials Machine Learning Tutorial Machine Learning Frameworks
Secondary Sidebar
Machine Learning Tutorial
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning Architecture
    • Automation Anywhere Architecture
    • Machine Learning C++ Library
    • Machine Learning Frameworks
    • Data Preprocessing in Machine Learning
    • Data Science Machine Learning
    • Classification of Neural Network
    • Neural Network Machine Learning
    • What is Convolutional Neural Network?
    • Single Layer Neural Network
    • Kernel Methods
    • Forward and Backward Chaining
    • Forward Chaining
    • Backward Chaining
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Naive Bayes in Machine Learning
    • Dataset Labelling
    • DataSet Example
    • Deep Learning Techniques
    • Dataset ZFS
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • What is Kernel in Machine Learning
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Machine Learning Certifications
    • Machine Learning Python vs R
    • Optimization for Machine Learning
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Support Vector Machine in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • Dataset for Linear Regression
    • Decision tree limitations
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Apriori Algorithm in Machine Learning
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • Hierarchical Clustering Agglomerative
    • What is a Greedy Algorithm?
    • What is Genetic Algorithm?
    • Random Forest Algorithm
    • Nearest Neighbors Algorithm
    • Weak Law of Large Numbers
    • Ray Tracing Algorithm
    • SVM Algorithm
    • Naive Bayes Algorithm
    • Neural Network Algorithms
    • Boosting Algorithm
    • XGBoost Algorithm
    • Pattern Searching
    • Loss Functions in Machine Learning
    • Decision Tree in Machine Learning
    • Hyperparameter Machine Learning
    • Unsupervised Machine Learning
    • K- Means Clustering Algorithm
    • KNN Algorithm
    • Monty Hall Problem
  • Supervised
    • What is Supervised Learning
    • Supervised Machine Learning
    • Supervised Machine Learning Algorithms
    • Perceptron Learning Algorithm
    • Simple Linear Regression
    • Polynomial Regression
    • Multivariate Regression
    • Regression in Machine Learning
    • Hierarchical Clustering Analysis
    • Linear Regression Analysis
    • Support Vector Regression
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Deep Learning
    • What Is Deep learning
    • Overviews Deep Learning
    • Application of Deep Learning
    • Careers in Deep Learnings
    • Deep Learning Frameworks
    • Deep Learning Model
    • Deep Learning Algorithms
    • Deep Learning Technique
    • Deep Learning Networks
    • Deep Learning Libraries
    • Deep Learning Toolbox
    • Types of Neural Networks
    • Convolutional Neural Networks
    • Create Decision Tree
    • Deep Learning for NLP
    • Caffe Deep Learning
    • Deep Learning with TensorFlow
  • RPA
    • What is RPA
    • What is Robotics?
    • Benefits of RPA
    • RPA Applications
    • Types of Robots
    • RPA Tools
    • Line Follower Robot
    • What is Blue Prism?
    • RPA vs BPM
  • Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

Related Courses

Machine Learning Training

Deep Learning Training

Artificial Intelligence Training

Machine Learning Frameworks

By Priya PedamkarPriya Pedamkar

Machine-Learning-Frameworks

Introduction to Machine Learning Frameworks

Machine learning framework has been defined as a tool, library, or interface that gives developers the ease of creating machine learning models. Furthermore, the machine learning framework provides a standard way that the developers use while deploying these applications as the user can selectively change the generic functionality of the frameworks by their application code and thus follows the standard way of development of code.

All in One Data Science Bundle(360+ Courses, 50+ projects)
Python TutorialMachine LearningAWSArtificial Intelligence
TableauR ProgrammingPowerBIDeep Learning
Price
View Courses
360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (86,112 ratings)

Top 10 Different Machine Learning Frameworks

Given below are the top 10 different machine learning frameworks:

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

1. Scikit-Learn

It is a free machine learning library that is built on SciPy (scientific python). It is used very extensively by Python Programmers. David Cournapeau developed it. You can do feature engineering with your data (increasing the number of features), scaling, pre-processing, splitting your data into training and test subsets. It also includes many machine learning algorithms like Linear Regression, Logistic regression, K–mean algorithm, support vector machines. It is very popular because it can easily work with NumPy and SciPy.

2. Tensor Flow

It is also an open-source library that is generally used for deep learning or machine learning algorithms using neural networks. Google creates it. Tensor Flow is a library for data flow programming; It uses various optimization techniques for the calculation of the mathematical expression, which is used to get the desired results.

The salient features of sci-kit learn are:

  • It works great with a mathematical expression that involves multidimensional arrays.
  • It is highly scalable across machines.
  • It works with a wide variety of data sets.
  • These features make it a very useful framework for deploying production models.

3. Amazon Machine Learning

As the name suggests, it is provided by Amazon. It is a service that developers can use to create models. It can be used as a visualization tool and can be used by machine learning engineers to create models without knowing every model’s very detail. It can run or create all kinds of models like Binary classification, multi-class classification ensemble algorithms, regression models.

4. Azure ML Studio

This framework comes from Microsoft. So how it works is that it allows registered Azure users to create and train models, and after having done that, you can use them as APIs to be consumed by other services. Users get up to 10GB of storage per account. It supports a wide variety of machine learning algorithms. One very good feature about this is that even if you do not have an account, you can try out the service by logging in to the account anonymously, and you can use ML studio for up to 8 hours.

5. MLib (Spark)

It is Apache Spark’s machine learning product. It contains or supports all types of machine learning algorithms and utilities like regression classification (binary and multi-class), clustering, ensemble and many more.

6. Torch

It is a scientific machine learning framework that supports various machine learning utilities and algorithms. The salient feature of this framework is that it puts GPU first. It has community-driven packages in machine learning, computer vision, image processing, deep learning and many more. Its main is to provide high scalability, flexibility, and speed while creating machine learning models. It is definitely a framework to look for while building machine learning models.

7. Theano

It is built using python. It allows us to define, create and optimize mathematical calculations. Like Torch, It can also use GPU, which helps in optimization and scalability.

8. Veles

It is written in C++, and it is a deep learning framework. Though it is written in C++, It does use python to perform automation. It is mainly used in neural networks like CNN(convolution Neural Networks) recurrent neural networks.

9. H20

The name sounds interesting, but this framework allows us to apply maths and predictive analytics to solve today’s problems.

Moreover, it uses some combines some cool features like:

  • Best of Breed Open Source Technology.
  • Easy to use WebUI.
  • Data Agnostic Support for all common databases.
  • Along with using H2o, we can work on with existing languages and also extend it seamlessly with Hadoop.

10. Caffe

It is a deep learning framework that was created using speed, modularity in mind. It is mainly used with neural network problems and was founded by Berkeley Vision and Learning Center.

Conclusion

Every field today produces data, and data needs to be analyzed and modeled using certain algorithms so that it can be used to produce better future results. So, in short, that’s what machine learning does. It is an essential skill of the 21st century, and most of the frameworks are open-source with developer communities. It is one of the growing fields in technology and the IT field.

Recommended Articles

This has been a guide to Machine Learning Frameworks. Here we have discussed the introduction and top 10 different machine learning frameworks. You may also look at the following article to learn more –

  1. Machine Learning Techniques
  2. Introduction To Machine Learning
  3. Neural Network Machine Learning
  4. Machine Learning Feature Selection
Popular Course in this category
Machine Learning Training (20 Courses, 29+ Projects)
  19 Online Courses |  29 Hands-on Projects |  178+ Hours |  Verifiable Certificate of Completion
4.7
Price

View Course

Related Courses

Deep Learning Training (18 Courses, 24+ Projects)4.9
Artificial Intelligence AI Training (5 Courses, 2 Project)4.8
1 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more