EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login

Machine Learning Tools

Home » Data Science » Data Science Tutorials » Machine Learning Tutorial » Machine Learning Tools

machine learning tool

Introduction to Machine Learning

Machine learning tools (Caffee 2, Scikit-learn, Keras, Tensorflow, etc.) are defined as the artificial intelligence algorithmic applications that give the system the ability to understand and improve without being explicitly programmed. as these tools are capable of performing complex processing tasks such as the awareness of images, speech-to-text, generating natural languages, etc. These tools are used for applications in which training wheels (where the individual schedules input and the desired output) are used the termed as supervised algorithm while the tools without training wheels are unsupervised algorithms and the selection of these machine learning tools entirely depends upon the type of algorithm that needs to be used for the application

What is Machine Learning Tool?

Machine learning tools are artificial intelligence-algorithmic applications that provide systems with the ability to understand and improve without considerable human input. It enables software, without being explicitly programmed, to predict results more accurately.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

It with training wheels are supervised algorithms. They require an individual to schedule both the input and the desired output and provide feedback on the accuracy of the end results. Unsupervised algorithms demand very little human intervention by employing a “deep learning” approach in order to check massive databases and arrive at conclusions from previous example-based data of training; they are thus generally used for more complex processing tasks, such as the awareness of images, speech-to-text and generating natural languages.

Machine Learning Tools are consists of

  1. Preparation and data collection
  2. Building models
  3. Application deployment and Training

Local tools for telecommunication and remote learning

We can compare machine learning tools with local and remote. You can download and install a local tool, and use it locally, but a remote tool runs on an external server.

Local Tools

You can download, install and run a local tool in your local environment.

Characteristics of Local Tools are as follows:

  1. Adapted for data and algorithms in-memory.
  2. Configuration and parameterisation execution control.
  3. Integrate your systems to satisfy your requirements.

Examples of Local Tools are Shogun, Golearn for Go, etc.

Popular Course in this category
Machine Learning Training (17 Courses, 27+ Projects)17 Online Courses | 27 Hands-on Projects | 159+ Hours | Verifiable Certificate of Completion | Lifetime Access
4.7 (8,485 ratings)
Course Price

View Course

Related Courses
Deep Learning Training (15 Courses, 24+ Projects)Artificial Intelligence Training (3 Courses, 2 Project)

Remote Tools

This tool is hosted from the server and called to your local environment. These instruments are often called Machine Learning as a Service (MLaaS)

  1. Customized for larger datasets to run on a scale.
  2. Execute multiple devices, multiple nuclei, and shared storage.
  3. Simpler interfaces that provide less configuration control and parameterizing of the algorithm.

Examples of these Tools are Machine Learning in AWS, Predication in Google, Apache Mahout, etc.

Tools for Machine Learning

Below are the different tools that are as follows:

tensorflow

TensorFlow

This is a machine learning library from Google Brain of Google’s AI organization released in 2015. Tensor Flow allows you to create your own libraries. We can also use C++ and python language because of flexibility. An important characteristic of this library is that data flow diagrams are used to represent numerical computations with the help of nodes and edges. Mathematical operations are represented by nodes whereas edges denote multidimensional data arrays on which operations are performed. TensorFlow is used by many famous companies like eBay, Twitter, Dropbox, etc. It also provides great development tools, especially in Android.

keras

Keras

Keras is a deep-learning Python library that can run on top of Theano, TensorFlow. Francois Chollet, a member of the Google Brain team, developed it to give data scientists the ability to run machine learning programs fast. Because of using the high-level, understandable interface of the library and dividing networks into sequences of separate modules, rapid prototyping is possible. It is more popular because of the user interface, ease of extensibility and modularity. It runs on CPU as well as GPU.
scikit learn

Scikit-learn

Scikit-learn, which was first released in 2007, is an open source library for machine learning. Python is a scripting language of this framework and includes several models of machine learning such as classification, regression, clustering, and reduction of dimensionality. Scikit-learn is designed on three open source projects — Matplotlib, NumPy, and SciPy.

Scikit-learn provides users n number of machine learning algorithms. The framework library focuses on data modeling but not on loading, summarizing, manipulating data.
caffe 2

Caffe2

Caffe2 is an updated version of Caffe. It is a lightweight, open source machine learning tool developed by Facebook. It has extensive machine learning library to run complex models. Also, it supports mobile deployment. This library has C++ and Python API which allows developers to prototype first, and optimization can be done later
Machine Learning Tools - apache spark

Apache Spark MLlib

Apache Spark MLlib is a distributed framework for machine learning. The Spark core is developed at the top. Apache sparks MLlib is nine-time faster from disk-based implementation. It is used widely as an open source project which makes focus on machine learning to make it easy.

Apache Spark MLlib has a library for scalable vocational training. MLlib includes algorithms for regression, collaborative filters, clustering, decisions trees, pipeline APIs of higher levels.
Machine Learning Tools - opennn

OpenNN

OpenNN is developed by the artificial intelligence company Artelnics. OpenNN is an advanced analytics firmware library written in C++. The most successful method of machine learning is the implementation of neural networks. It is high in performance. The execution speed and memory allocation of this library stand out.

Machine Learning Tools - amazon sagemaker

Amazon SageMaker

Amazon SageMaker is a fully managed service that allows data researchers and developers to build, train and implement machine learning models in any scale quickly and easily. Amazon SageMaker supports open-source web application Jupyter notebooks that help developers share live code. These notebooks include drivers, packages and libraries for common deep learning platforms and frameworks for SageMaker users. Amazon SageMaker optionally encrypts models both during and during transit through AWS Key Management Service, and API requests are performed over a secure connection to the socket layer. SageMaker also stores code in volumes that are protected and encrypted by security groups.

Conclusion

Before developing machine learning applications, it is very important to select a machine learning tool which has extensive libraries, great user interface and support for common programming languages. So this has been a guide to Machine learning tools which will help in selecting required technology.

Recommended Articles

This has been a guide to Machine learning tools. Here we have discussed the Tools for machine learning and the local tools for telecommunication and remote learning. You can also go through our other suggested articles to learn more-

  1. What is Machine Learning?
  2. Machine Learning Techniques
  3. Careers in Machine Learning
  4. Machine Learning vs Statistics
  5. Matplotlib In Python

Machine Learning Training (17 Courses, 27+ Projects)

17 Online Courses

27 Hands-on Projects

159+ Hours

Verifiable Certificate of Completion

Lifetime Access

Learn More

0 Shares
Share
Tweet
Share
Primary Sidebar
Machine Learning Tutorial
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • 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
    • Linear Regression Modeling
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Linear Regression Analysis
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning 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
  • Deep Learning
    • What Is Deep learning
    • 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
  • Pytorch
    • PyTorch Versions
    • Single Layer Perceptron
    • PyTorch vs Keras
    • torch.nn Module
  • UiPath
    • What is UiPath
    • UiPath Careers
    • UiPath Architecture
    • UiPath Orchestrator
    • Uipath Reframework
    • UiPath Studio
  • Interview Questions
    • Machine Learning Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

Related Courses

Machine Learning Training

Deep Learning Training

Artificial Intelligence Training

Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • 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

© 2020 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

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
Book Your One Instructor : One Learner Free Class

Let’s Get Started

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

EDUCBA

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

Forgot Password?

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

Special Offer - Machine Learning Training (17 Courses, 27+ Projects) Learn More