Top 20 Machine Learning Frameworks

What is Machine Learning?

Machine Learning, ML for short, is an area of computational science that deals with the analysis and interpretation of patterns and structures in large volumes of data. Through it, we can infer insightful patterns from data sets to support business decision-making – without or with very little need for human interface.

In Machine Learning, we feed large volumes of data to a computer algorithm that then trains on it, analyzing it to find patterns and generating data-driven decisions and recommendations. If there are any errors or outliers in information identified, the algorithm is structured to take this new information as input to improve its future output for recommendations and decision-making.

Simply put, ML is a field in AI that supports organizations to analyze data, learn, and adapt on an ongoing basis to help in decision-making. It’s also worth noting that deep learning is a subset of machine learning.

What is a Machine Learning Framework?

A simplified definition would describe machine learning frameworks as tools or libraries that allow developers to easily build ML models or Machine Learning applications, without having to get into the nuts and bolts of the base or core algorithms. It provides more of an end-to-end pipeline for machine learning development.

Here are the top 20 machine learning frameworks:

  1. TensorFlow
  2. PyTorch
  3. scikit-learn
  4. Keras
  5. MXNet
  6. Caffe
  7. Theano
  8. Microsoft Cognitive Toolkit (CNTK)
  9. Spark MLlib
  11. LightGBM
  12. XGBoost
  13. CatBoost
  15. Torch
  16. CNTK (Microsoft Cognitive Toolkit)
  17. Deeplearning4j
  18. Mahout
  19. Accord.NET
  20. Shogun

1. TensorFlow:

Developed by Google’s Brain Team, TensorFlow is one of the most widely used machine learning frameworks. It provides a comprehensive ecosystem for building and deploying machine learning models, including support for deep learning. TensorFlow offers high-level APIs for ease of use and low-level APIs for customization.

Key Features:

  • Based on JavaScript
  • Open source and has extensive APIs
  • Can be used via script tags or via installation through npm
  • Runs on CPUs and GPUs
  • Extremely popular and has lots of community support

2. PyTorch:

PyTorch is a popular open-source machine learning framework developed by Facebook’s AI Research Lab. It has gained significant popularity due to its dynamic computational graph, which enables more flexibility during model development. PyTorch is widely used for research purposes and supports both deep learning and traditional machine learning models.

Key Features:

  • Supports cloud-based software development
  • Suitable for designing neural networks and Natural Language Processing
  • Used by Meta and IBM
  • Good for designing computational graphs
  • Compatible with Numba and Cython

3. scikit-learn:

scikit-learn is a Python library that provides a simple and efficient set of tools for data mining and machine learning. It offers a wide range of algorithms for classification, regression, clustering, and dimensionality reduction. scikit-learn is known for its user-friendly API and extensive documentation.

Key Features:

  • Works well with Python
  • The top framework for data mining and data analysis
  • Open-source and free

4. Keras:

Keras is a high-level neural networks API written in Python. Initially developed as a user-friendly interface for building deep learning models on top of TensorFlow, Keras has evolved into an independent framework. It provides an intuitive and modular approach to building neural networks and supports both convolutional and recurrent networks.

5. MXNet:

MXNet is a deep learning framework that emphasizes efficiency, scalability, and flexibility. It offers both imperative and symbolic programming interfaces, allowing developers to choose the approach that best suits their needs. MXNet is known for its support of distributed training, which enables training models on multiple GPUs or across multiple machines.

Key Features:

  • Adopted by Amazon for AWS
  • Supports multiple languages, including Python, JavaScript, Julia, C++, Scala, and Perl
  • Microsoft, Intel, and Baidu also support Apache MXNet
  • Also used by the University of Washington and MIT

6. Caffe:

Keeping speed, modularity, and articulation in mind, Berkeley Vision and Learning Center (BVLC) and community contributors came up with Caffe, a Deep Learning framework. Its speed makes it ideal for research experiments and production edge deployment. It comes with a BSD-authorized C++ library with a Python interface, and users can switch between CPU and GPU. Google’s DeepDream implements Caffe. However, Caffe is observed to have a steep learning curve, and it is also difficult to implement new layers with Caffe.

7. Theano:

Theano was developed at the LISA lab and was released under a BSD license as a Python library that rivals the speed of the hand-crafted implementations of C. Theano is especially good with multidimensional arrays and lets users optimize mathematical performances, mostly in Deep Learning with efficient Machine Learning Algorithms. Theano uses GPUs and carries out symbolic differentiation efficiently.

Several popular packages, such as Keras and TensorFlow, are based on Theano. Unfortunately, Theano is now effectively discontinued but is still considered a good resource in ML.

8. Microsoft Cognitive Toolkit (CNTK):

CNTK is a deep learning framework developed by Microsoft. It provides high-level abstractions and supports both convolutional and recurrent neural networks. CNTK is known for its scalability and performance, particularly in distributed training scenarios.

9. Spark MLlib :

Spark MLlib is a machine learning library provided by Apache Spark, an open-source big data processing framework. Spark MLlib offers a wide range of tools and algorithms for building scalable and distributed machine learning models. It is designed to work seamlessly with the Spark framework, enabling efficient processing of large-scale datasets.

10. : is an open-source machine-learning platform that provides a range of tools and frameworks for building and deploying machine-learning models. It aims to make it easy for data scientists and developers to work with large-scale data and build robust machine-learning pipelines.

11. LightGBM:

LightGBM is an open-source gradient-boosting framework developed by Microsoft. It is specifically designed to be efficient, scalable, and accurate, making it a popular choice for various machine-learning tasks.

12. XGBoost:

XGBoost (Extreme Gradient Boosting) is a powerful and widely used open-source gradient boosting framework that has gained significant popularity in the machine learning community. It is designed to be efficient, scalable, and highly accurate for a variety of machine-learning tasks.

13. CatBoost:

CatBoost is an open-source gradient-boosting framework developed by Yandex, a Russian technology company. It is specifically designed to handle categorical features in machine learning tasks, making it a powerful tool for working with structured data.

14. is a comprehensive deep-learning library and educational platform that aims to democratize and simplify the process of building and training neural networks. It provides a high-level API on top of popular deep learning frameworks like PyTorch, allowing users to quickly prototype and iterate on their models.

15. Torch:

Torch, or PyTorch, is a widely used open-source deep learning framework that provides a flexible and efficient platform for building and training neural networks. It is developed and maintained by Facebook’s AI Research Lab (FAIR).

16. CNTK (Microsoft Cognitive Toolkit):

CNTK (Microsoft Cognitive Toolkit), now known as Microsoft Machine Learning for Apache Spark, is an open-source deep learning framework developed by Microsoft. It provides a flexible and scalable platform for building, training, and deploying deep learning models.

17. Deeplearning4j:

Deeplearning4j (DL4J) is an open-source deep-learning library specifically designed for Java and the Java Virtual Machine (JVM) ecosystem. It provides a comprehensive set of tools and capabilities for building and training deep neural networks in Java, while also supporting integration with other JVM-based languages like Scala and Kotlin.

18. Mahout:

Apache Mahout is an open-source machine learning library and framework designed to provide scalable and distributed implementations of various machine learning algorithms. It is part of the Apache Software Foundation and is built on top of Apache Hadoop and Apache Spark, making it well-suited for big data processing.

19. Accord.NET:

Accord.NET is an open-source machine learning framework for .NET developers. It provides a wide range of libraries and algorithms for various machine-learning tasks, including classification, regression, clustering, neural networks, image processing, and more. Accord.NET aims to make machine learning accessible and easy to use within the .NET ecosystem.

20. Shogun:

Shogun is an open-source machine-learning library that provides a comprehensive set of algorithms and tools for a wide range of machine-learning tasks. It is implemented in C++ and offers interfaces for several programming languages, including Python, Java, Octave, and MATLAB.

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What is Prometheus & grafana and best course and certification in India?

Prometheus is an open-source system monitoring and alerting toolkit originally built at SoundCloud. Many companies and organizations have adopted Prometheus and have a very active developer and user community in the project. It is now a standalone open-source project and is maintained independently of any company.

Features of Prometheus:

  • A multi-dimensional data model with time series data identified by metric name and key/value pairs.
  • PromQL, a flexible query language to leverage this dimensionality.
  • No reliance on distributed storage; single server nodes are autonomous.
  • Time series collection happens via a pull model over HTTP.

What is Grafana?

Grafana is an open-source solution for running data analytics, understanding large amounts of data and monitoring our apps with the help of cool customizable dashboards.

What are the benefits of Grafana?

  • Customizable dashboards are feature-rich and can be configured to display data from a wide range of databases using visualization tools such as heatmaps, histograms, and charts.
  • The platform is flexible and easy to use.
  • Native support of a broad range of databases.

Grafana Features:

  • Dashboard templating – This is a Grafana feature that is really useful. It allows users to create a dashboard setup to suit their every need.
  • Provisioning – It can be simple enough to set up a single dashboard with a few clicks, dragging and dropping, but some users need even more ingenuity this way.
  • Annotations – This Grafana feature lets you mark graphs, which is especially helpful when you need to correlate data in case something misbehaves.
  • Custom plugins – You can extend the functionality of Grafana with plugins that provide additional tools, visualizations, and more.
  • Teams and permissions – Where an organization has an instance of Grafana and multiple teams, they usually prefer to have the option of implementing some dashboard isolation.

Pre-requisites to learn Prometheus and Grafana:

  • Basic experience with Linux/Unix system administration.
  • Familiarity with common shell commands, such as ls, cd, curl, etc.
  • Some knowledge and/or development experience in Go and Python.
  • Some experience working with Kubernetes.

If you are planning to learn Prometheus and Grafana, go with DevOpsSchool institute. Will provide you online and classroom training and certification course Program by an expert. To get in-depth knowledge of Prometheus and Grafana along with its various applications, check out our interactive, live-online training.

Here you can see the Agenda of Prometheus and Grafana Course:

This course covers the basics of Prometheus and Grafana and their main features. You will develop a critical understanding of why Prometheus and Grafana are useful and how they can be combined with other web development frameworks.

You will learn about the basics of Prometheus and Grafana and their advantages. Then, you’ll cover more advanced topics like integrating Prometheus and Grafana with other frameworks.

Hopefully, I think this information is helpful for you.

However, if you want to find out more details, be sure to check out our Prometheus and Grafana course to learn more about this technology.

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Getting Started With SpringBoot Application


Spring Boot is associated open-source Java-based framework wont to produce a small Service. It’s developed by the crucial Team and is employed to create complete and production-ready spring applications. This chapter can offer you an introduction to Spring Boot and familiarizes you with its basic ideas.

What is Spring Boot?
Spring Boot provides a decent platform for Java developers to develop a complete and production-grade spring application that you simply will just run. You’ll start with minimum configurations while not the requirement for a whole Spring configuration setup.

Spring is that the most popular and innovative community for Java developers. They need to modify the method how to build enterprise applications and how to manage them. Spring Boot is their latest innovation to stay up to now with the dynamic technology desires. The first motivation behind developing Spring Boot is to simplify the method for configuring and deploying the spring applications.

Why do we need Spring Boot?

  • Spring Boot is a next-generation attempt to easy spring setup.
  • Spring Boot’s main benefit is configuring the resources based on what it finds in the classpath.
  • If your maven pom includes JPA dependencies and MYSQL driver, then Spring Boot can be created a persistence unit supported by MySQL. If you’ve added an internet dependency, then you may get Spring MVC organized with defaults.
  • When we point out defaults, Spring Boot has its own opinions. If you’re not specifying the main points, it’ll use its own default configurations. If you would like persistence, however, don’t specify anything in your pom file, then Spring Boot configures Hibernate as a JPA supplier with an HSQLDB database.

How does it work?

Some is also asking themselves however will Spring Boot has auto configurations and what will very that basically that actually mean? It really comes right down to three easy Spring Boot annotations that offer this feature:


Between each one of these annotations, Spring Boot is able to provide default project dependencies as well as allow for defaults to be overwritten.

@SpringBootApplication is used within the entry purpose of the application add the category it resides in has to get to the application main technique. The annotation is required and can offer every of the opposite two annotations to your Spring Boot application since the @SpringBootApplication includes each inside.

The @EnableAutoConfiguration does just that it provides each of the representing classes with the Automatic Configuration capability.

Lastly, the @ComponentScan will at initialization scan all the beans and package declarations.

Spring Starter Dependencies
Not solely will Spring Boot include annotations however it conjointly uses Spring Starter Dependencies to make sure that your application starts with the proper dependencies therefore so you can and then you’ll hit the bottom running so to talk.

Many times, as an application grows larger it is laborious to properly set up project dependencies, the Spring Boot Starter plugins can help facilitate dependency management. an example of a spring starter dependency is that the Spring Boot Starter internet dependency.

That will be used so your application can have Rest Endpoints written into your application. Overall, they assist contour the event of those applications so a team can begin from an additional advanced purpose therefore fewer holes are going to be conferred particularly with larger applications.

Spring Boot Features

Web Development
It is a well-suited Spring module for internet application development. we are able to simply produce a self-contained HTTP server using embedded tomcat, Jetty, or Undertow. we are able to use the spring-boot-starter-web module to start out and running applications quickly.

It is a class that provides a convenient way to bootstrap a Spring application which can be started from the main method. You can call start your application just by calling a static run() method.

Application Events and Listeners
Spring Boot uses events to handle a variety of tasks. It allows us to create factories file that is used to add listeners. we can refer to it by using the ApplicationListener key.

Admin Support
Spring Boot provides the facility to modify admin-related features for the appliance. It’s wont to access and manage applications remotely. We will modify it by simply exploiting the spring application admin-enabled property.

Externalized Configuration
Spring Boot permits us to externalize our configuration so we will work with a similar application in several environments. The application uses YAML files to externalize configuration.

Properties Files
Spring Boot provides a rich set of Application Properties. So, we are able to use that within the properties file of our project. The properties file is used to line properties like server-port = 8082 and lots of others. It helps to arrange application properties.

YAML Support
It provides a convenient way for specifying hierarchical configuration. It is a superset of JSON. The SpringApplication class automatically supports YAML. It is a successful alternative to properties.

Type-safe Configuration
A strong type-safe configuration is provided to manipulate and validate the configuration of the application. Application configuration is usually an important task that ought to be type-safe. We are able to conjointly use annotation provided by this library.

Spring Boot uses Common logging for all internal logging. Logging dependencies are managed by default. We must always not modification logging dependencies if there’s no needed customization is required.

Spring Boot applications are spring bases net applications. So, it’s secure by default with basic authentication on all HTTP endpoints. A rich set of Endpoints are obtainable for developing a secure Spring Boot application.

Spring Boot Benefits

  • Application monitoring
  • Auto-Configurable
  • Caching providers
  • Application configurations flexibility

Responsibilities of SpringBoot

Designs, codes or configures, tests, debugs, deploys, documents and maintains web service applications using a variety of software development toolkits, testing/verification applications, and other tools while adhering to specific development best practices and quality standards.

Advantages of Spring Boot

  • It creates stand-alone Spring applications that can be started using Java -jar.
  • It tests web applications easily with the help of different Embedded HTTP servers such as Tomcat, Jetty, etc. We don’t need to deploy WAR files.
  • It provides opinionated ‘starter’ POMs to simplify our Maven configuration.
  • It provides production-ready features such as metrics, health checks, and externalized configuration.
  • There is no requirement for XML configuration.
  • It offers a CLI tool for developing and testing the Spring Boot application.
  • It offers a number of plug-ins.
  • It also minimizes writing multiple boilerplate codes (the code that has to be included in many places with little or no alteration), XML configuration, and annotations.
  • It increases productivity and reduces development time.

The primary roles of Spring-Boot:

  • To provide a bunch of non-functional features/solutions that are very much common to large-scale projects (e.g. embedded servers, security, metrics, health checks, externalized configuration).
  • To provide a radically faster and widely accessible getting started development experience for all Spring development. Since the spring community has evolved so big, it is time to re-invent the way how spring applications are deployed in a much quicker turnaround time.
  • To be get started so quickly using the default values which are supported out of the box in the Spring Boot configurations.

Scope of SpringBoot

Scopes a single bean definition to any range of object instances. Scopes a single bean definition to the lifecycle of one HTTP request; that’s every and each HTTP request can have its own instance of a bean created off the rear of a single bean definition.


I hope this article has provided the basics of Spring Boot Applications. Keep visiting this page for my update on the Spring Boot releases and a list of features in each release. I hope this information provides enough details regarding Spring Boot. This page would be often updated and maintained with the newest information regarding the Spring Boot Application. If you’re searching for any specific information, please write it within the comments section.

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What is a Framework in Laravel?

What is a Framework?

A framework is a set of conceptual structures and guidelines, used to build something. A framework may include predefined classes and functions that can be used to process input, manage hardware devices, and interact with system software. The purpose of the framework is to allow developers to focus on building a unique feature for their projects rather than writing code from scratch.

Why use Framework?

  • Colection of tools
  • No need to start scratch
  • Save Time
  • Improve Productivity
  • Clean Code
  • Reusable Code
  • Testing
  • Debugging

What is Web Framework?

A Web Framework or web application helps to build Web Applications. A Web framework provides tools and libraries to simplify common web development operations. This can include web services, APIs, and other resources. Web Framework help with a variety of tasks, from templating and database access to session management and code reuse. More than 80% of web app frameworks follow the Model View Controller architecture.

Some Web Framework

  • Laravel
  • Codeigniter
  • Zend
  • Django
  • Spring
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What is a Web Framework, Why do we use Web Frameworks?

What is a Web Framework?

A Web Framework (WF) or Web Application Framework (WAF) which helps to build Web Applications.
Web frameworks provide tools and libraries to simplify common web development operations. This can include web services, APIs, and other resources.
Web frameworks help with a variety of tasks, from templating and database access to session management and code reuse.
More than 80% of all web app frameworks rely on the Model View Controller architecture.

Why do we use Web Frameworks?

  • Collection of tools
  • No need to start from scratch
  • Save Time
  • Improve Productivity
  • Clean Code
  • Reusable Code
  • Testing
  • Debugging

Some Web Framework

  • Laravel
  • Codeigniter
  • Zend
  • Django
  • Spring
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