Here, in this blog, we have listed out the top 20 all time Machine Learning frameworks that you should know if you're working with ML. We have discussed each framework, from Scikit Learn and PyTorch to ML Pack and Theano in detail. We have discussed their features, advantages, and how you can make best use of them. We have also answered some FAQ at the end of the article. Read in detail.
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Machine learning is going to be a new normal in the field of technology. As per the reports and analysis, Machine Learning will create more millions across the globe. Today, we will talk about various Machine Learning Frameworks.
It comes with huge benefits for the various industries. The best part of machine learning technology is the accuracy and efficiency it has to process the data.
Due to which the algorithms are enabled to make better predictions and decisions. In short, we can say that machine learning is the future of every business you can think of.
Scikit-Learn is one of the finest Machine Learning Frameworks developed by the Python Community. It can be used for supervised and unsupervised learning problems. When you think of using Python for AI & Machine Learning, you always think about Sckikit-Learn.
If you’ve some idea of how Python works, then this framework could be a blessing in disguise for you. Scikit-Learn can work on multiple tasks without any sacrifice in speed.
It has a clean API and it’s highly recommended for data mining problems. If you want to build models, then this is the best framework in the business.
It is one of the best open-source libraries, that is specially developed for data-based programming.
TensorFlow can be the best Machine Learning Framework to handle a wide range of data. The dreamworld supports classifications, complicated algorithms and regressions.
This machine learning framework can work well with CPUs and GPUs. The only thing to take note here is, you need to give extra effort in learning the TensorFlow as it is quite complex.
It has multiple versions as well as models which help you best to handle a large amount of data. It can run on every device, so it is portable and used for the development purpose.
PyTorch (also known as Torch) is an amazing machine learning framework, which has scripting language like Lua programming UI.
This machine learning framework comes with high-end flexibility and efficiency. Some of the most popular platforms which use PyTorch are Facebook, Yandex, IBM, Idiap Research Institute, and many more.
The framework comes with great ease to handle complicated operations. You can perform the difficult operations in less time by using this machine learning framework.
The torch is even known as the scientific computing framework to handle the machine learning algorithms. The best of Torch is the huge ecosystem of driven packages it has.
CAFFE (Convolutional Architecture for Fast Feature Embedding) is a machine learning framework that is developed with the help of CPP.
This framework is known for its faster processing of deep neural networks. It comes with a Mat Lab and Python interface which makes the functions work faster.
There is always a debate going on about R vs Python for Machine Learning solutions. But, Python has more trust from the developer community.
This machine learning framework exactly knows how to render research experiments as well as industrial deployment. Due to all these specialties of CAFFE, it is also known as a deep learning tool for machine learning.
Microsoft CNTK (Cognitive Toolkit) is a deep learning toolkit provided by Microsoft. This framework is built to understand the algorithm for the biological human brain.
It works well with Artificial Intelligence and Machine Learning . You can even use the machine learning framework to create models for your business.
Microsoft Cognitive Toolkit can even be customized as per your needs. You can choose your networks, metrics, algorithms, and more.
It even enables support to multi-machine-multi-GPU-back-ends. This one is a great machine learning framework if you want your own customization model. And take note of a point that this tool works perfectly for the algorithms built for the human brain.
Keras is one of the best machine learning frameworks which acts as a bridge between the machine and deep learning.
The most common usage of this framework is summarization and classification. The framework is really easy to learn and suits best to the Python developers.
It is even known as a simple machine learning framework which comes with an understanding of translation, tagging, understanding of various languages and much more.
You can even use Keras with TensorFlow, as both work perfectly well with each other. When you are looking forward to building a model-based on classification, Keras will suit best to your needs.
This is a new machine learning tool that comes with extraordinary functionalities. Firebase machine learning kit offers a multi-platform working.
That means, it enables you to handle the text recognition, language handling, summarization, classifications of objects, and more multiple platforms.
The machine learning framework comes with pre-trained models to handle multiple platforms. It enables developers to build minimal coded projects.
The reason why Google launched this machine learning framework, is to enable the developers to integrate the models into mobile applications.
And the best thing is there is no need to have the technical expertise to integrate it. Therefore, if you’re planning for any such integration, this tool will work best for you.
It is a perfect machine learning framework built for huge data analysis. The framework contains big deep learning models over the database.
Apache Singa is designed with the help of an intuitive programming model. This programming model is based on the abstraction layer.
The various deep learning models supported by these frameworks are Recurrent Neural Network (RNN), restricted Boltzmann machine (RBM) and Convolutional Neural Networks (CNN).
With all these, many in-built layers are provided for users of the framework. It is one of the best machine learning frameworks for huge data learning.
It is an amazing framework which makes it easy for the developers to understand every model of the machine learning technology.
It is complete for all those who have just started with machine learning or interested in learning new things.
Amazon Machine Learning comes with various wizards and visualization tools, which helps the developers to build the best machine learning. This tool guides a developer of every phase of developing machine learning.
With this framework, developers do not have any need to learn the complex model for the development of machine learning.
The framework can run so many things like multiclass categorization, regression, binary classification, and much more to create a model.
The machine learning framework allows Microsoft users to seamlessly create models and train them. They can even convert the model into APIs and integrate them into other services.
With this machine learning framework, Microsoft users can get approximately 10 GB of storage to keep their model. This storage is based per account.
To store larger models, you can even connect your Azure account to the service. There is a wide range of algorithms available with Azure ML Studio which works for Microsoft as well as third parties.
The fact is you even don’t need an account to use this framework for up to 8 hours.
It is the type of machine learning framework which helps the developer to apply math to solace any problem of today’s world.
H2O even handles the predictive analysis to solve real-world business challenges. You can easily build the machine learning model for such cases with the help of this framework.
The framework smartly combines uniques features with the best possible solution. The best thing about using a H2O machine learning tool is you can easily use it with your preferred language and tools.
There are no restrictions for using this framework. And that is one of the reasons why it is popular among the developers of machine learning.
It is one of the most popular machine learning frameworks for data analysis. Massive Online Analysis is even an open-source framework for data mining. This is an active community used by a lot of developers.
The framework includes machine learning algorithms like outlier detection, clustering, regression, recommender system, classifications, and many more.
With all these, it even contains various tools that help for the evaluation of machine learning. Massive Online Analysis (MOA) is written in Java, which works as an extra benefit for scaling problems.
If you’re into data streaming for machine learning, then this tool works best for you.
ML lib is a machine learning library by Apache Spark. The main goal of this framework is to make practical machine learning easy and scalable.
The framework consists of many learning algorithms which are commonly used for the development. Some of those algorithms are outlier detection, clustering, regression, recommender system, classifications, and many more
The machine learning framework even contains some higher-level pipeline APIs. If you’re inclined more towards the easiness of practical machine learning, this framework is for you.
It is a machine learning library which is built with the help of the C++. The framework was launched in 2011 which was later updated. Mlpack is specially designed for ease of use, scalability, and speed of machine learning.
MLpack frameworks will provide you algorithm, with single line commands. You can even integrate the C++ classes, to work with a higher level of machine learning programs.
That is, the framework provides an easy integration of C++ with complex machine learning development. Mlpack is of the best machine learning frameworks you can think of to handle the complexity.
It is one of the web mining modules for machine learning. This ML framework is built with the use of the Python programming language.
The pattern has a tool which helps to deal with the mining data of various popular platforms like Wikipedia, Google, Twitter, HTML DOM parser, a web crawler, and much more.
The machine learning framework can even handle various models like the vector space model, SVM, clustering, visualization, network analysis, and much more.
Pattern suits best to all those developers who have expertise in Python programming language.
So, if web mining through machine learning is your ultimate target, then this framework is for you.
It is one of the oldest machine learning frameworks which was launched back in 1999. The fact here is, the framework is written in C++ but it is not just limited to this programming language.
You can use the machine learning frameworks like Shogun with various programming languages like C#, Ruby, R, Java, Python, Lua, Octave, and Matlab.
This framework has unlimited capabilities. It comes with a broad range of features which makes your machine learning development much easier.
The various machine learning algorithms provided by the Shogun are exploratory data analysis, regression, classification and many more. Shogun is one of the most trusted machine learning frameworks.
Theano is a machine learning framework which contains the Python library. This framework enables you to analyse and optimize the mathematical expressions.
Theano has specialized in solving the mathematical expression which contains multi-dimensional arrays.
Theano gets its name after a great mathematician from Greek. You can solve any mathematical complexity with the help of this machine learning framework.
It even helps with the rapid development of some complex algorithms in machine learning. The implementation of this framework is quite faster in comparison to other frameworks.
It is a well-distributed machine learning framework to solve complex issues. Veles is also as a deep learning framework with lots of benefits.
The framework takes its basis on C++ and even uses Python to perform some tasks. Veles help to maintain perfect coordination between the automation of the codes.
You can easily normalize the database with the help of this framework. You can directly use the models in the productions.
The main goal of machine learning frameworks like Veles is to achieve flexibility and higher performance.
Apache Spark is one of the best machine learning frameworks to handle cluster computation.
This machine learning framework takes its basis on Java, R, Python, and Scala. It is capable of running from small to large machine codes.
Due to its ease of use, the framework is popular among the developers. The framework offers innumerable simple API for the one who knows R & python.
This framework will be best to all those developers who have knowledge of these two programming languages. Apache Spark can easily run the machine learning codes on almost every machine.
Apache Mahout is an excellent machine learning tool for statisticians, data scientists, mathematicians, and more. This framework lets a user perform the algorithms rapidly.
The main focus of this machine learning framework is classification, collaboration grouping, analysis, and filtering.
The best part of Apache Mahout is it can even run on big data platforms. And the developers can even shift the codes to the implementations as well as applications.
Apache Mahout can even climb on Apache Hadoop with the help of some integration or reduce paradigm.
With all the above facilities, the framework even provides a statistics engine and linear algebra (distributed).
This could be the best choice if you are looking for developing machine learning with scalable performance and need the above-given functionalities for the same.
Machine Learning is the future of almost every industry you can think of. And these above given 20 machine learning frameworks can help you best to achieve your aim.
All of them come with special functions which you can implement in your machine learning based on your needs. Henceforth, now you have enough tools to build the best machine learning app for your business.
All thanks to the developers of these machine learning frameworks who really made it easy for us to build the machine learning apps.
We hope you had a great experience reading this article and it proves to be of great benefit for any Python Developer. Thank You.!
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