Best Frameworks in 2020: The epoch of Machine Learning and Artificial intelligence have been arrived in each and every aspect of our life. There is no portion left either in our life or in our business industry where we did not get a chance to experience the importance and significance of Machine Learning and Artificial Intelligence. Day to day it’s improving a lot of progress in the Technological field.
To strengthen the process, operations and be ahead of the competition, most of the companies and organizations are moving to ML and AI.
With the enlargement of machine learning and AI, Organizations are offering a smart and quick solution and predictive customization to their consumers. Although, due to different reasons like lack of resources, lack of expected data, etc some companies are not getting the privilege to implement machine learning and AI for their processes.
Before moving forward to know the top Machine learning frameworks, we will understand what is Machine Learning? and the type of machine learning.
What is Machine Learning?
Machine Learning (ML) is a core subset of Artificial Intelligence (AI) that allows software applications to study from the given data and become more precise in predicting conclusions without any human intercession. Machine learning is a mechanism for converting information into knowledge.
Machine learning is permitting computers to deal with the operations that have, until now, only been performed by people.
It is a rising passion for developers that give focus to data analysis and data science.
Machine learning utilizes algorithms to study from the data and produces a model to be used in an application. To accomplish the wanted output, knowing about the algorithms is very essential but building them is not needed as many organizations offer a framework that provides all the tools and functionalities to meet the desired output in a quick and efficient way.
Machine learning is used in every field of business that produces a large amount of data which is a bit difficult for the human to handle. Government field, Retail industries, Health Care, Transportation, and Travel, etc are the industries that are utilizing the benefits of Machine Learning.
Machine Learning is tricky in itself, that’s why it has been separated into three areas, Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Supervised Learning: Supervised learning is a type of Machine Learning which uses known or labeled data for machine training. A supervised learning algorithm learns from “Labelled” training data and will help you to guess the result for unpredicted data. This learning allows us to store and produce a data output from the previous experience.
It will help you to optimize performance criteria using experience and will resolve different types of real-world estimation problems.
Unsupervised Learning: Unsupervised learning is a type of machine learning, where models are not needed to be supervised. Instead of that, you will have to permit the machine to work on its own to found the information. Unsupervised Learning algorithm deals with the unlabelled data means no one has seen the data before. Unsupervised machine learning help to find all type of the unknown patterns in data and also help to find features which can be useful for categorization.
Reinforcement Learning: In reinforcement learning, there is no solution found for the dataset but the reinforcement factor determines what to perform with the given task. Due to lack of training dataset, reinforcement tied to learn from its experience.
5 Best Machine Learning – Best Frameworks in 2020
A Machine Learning Framework is a touch-point, library or instrument which permits developers to develop machine learning models easily and smoothly without getting into the depth of the fundamental algorithms.
Each framework is created in various modes for different objectives. Listed below are the Top and Best Machine learning frameworks which will be the perfect fit in resolving your business requirements and challenges as per the need.
1. TensorFlow – Best Frameworks in 2020
TensorFlow is created by Google for augmenting research tasks and production operations. It also provides extensive flexible features, an enormous programming library, and other resources for plenty of development tasks. TensorFlow could be used on a wide range of materials like Gmail, Google Photos, Speech Recognition, etc. The framework can execute complex research on Machine learning.
TensorFlow uses Python, R like languages and dataflow graphs to process the data. It is very crucial because when you will develop these neural networks, you will able to see how the data flows through the neural network.
These machine learning frameworks are easy to build, can be used for strong machine learning production, and allow robust experimentation for research.
2. Apache Mahout
Apache Mahout is a machine learning framework, which uses linear algebra to write and implement ML algorithms.
Apache Mahout is a Deep Learning framework that is built by Apache Software Foundation and runs on the top of Apache Hadoop. It is an open-source and free platform that utilizes the MapReduce model and operates on a distributed linear algebra framework.
Being one of the best frameworks of machine Learning, Apache Mahout provides patterns and different libraries, for programming, analysis and more.
The apache scalable algorithms are for data classification, clustering, and batch-based collaborative filtering.
PyTorch or Torch is an open-source machine learning library, a scientific computing framework, developed by Facebook.
PyTorch is a scripting language based on the Lua programming which offers a broad range of algorithms for deep learning, and the scripting language LuaJIT language. The framework is strongly famous for its simplicity, flexibility, and customizability.
Basically, It is a Python ML package structured on Torch which is an open-source machine learning package based on the programming language Lua.
It is utilized to design neural networks by using Autograd Module and NPL and will be the top choice for designing computational graphs.
4. Sci-Kit Learn
Sci-Kit Learn is another very well-found machine learning framework among Python developers. It is also open-source and free to use framework even in trade and business. Sci-Kit Learn framework is an ML framework which is very simple to learn and utilize for the developers.
The library is built upon SciPy (Scientific Python) library and the framework includes NumPy, Matplotlib, Sympy and Pandas libraries.
This framework offers a wide range of supervised and unsupervised learning algorithms. Sci-Kit Learn is one of the topmost Machine Learning frameworks which can be used for data mining and data analysis for the organizations.
Shogun is one more machine learning, open-source framework that is compatible with the C++ programming language.Although, Shogun doesn’t only support C++, but also Lua, MatLab, Python, R, Java, C#, Ruby, and more.
This open-source framework permits developers to link with many machine learning libraries that include, LibLinear, LibSVM, SVMLight, and more.Shogun is very easy to use a framework that provides the advantages of assisting in Hidden Markov implementation, give the capability to process large sets of data, and has yielding features which provide developers to easily, quickly learn and implement.
These are the 5 best Machine Learning frameworks that can be used in 2020 as per the requirement. There are also some other frameworks that are available under Machine Learning and can be used as per the required algorithm, your expertise, and the client’s budget. Selecting the correct ML framework wisely can decrease the time and effort of a developer.