Machine Learning (ML) is the study of computer algorithms that improve automatically through experience and using data.
Machine learning algorithms build a model based on sample data, known as “training data”, to make predictions or decisions without being explicitly programmed to do so.
The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available.
Machine learning approaches are traditionally divided into three broad categories names as Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models.
Top 6 Machine Learning Frameworks:
Either you are a researcher, start-up, or big organization that wants to use machine learning, you will need the right tools to make it happen.
Machine learning frameworks are used in the domains related to computer vision, natural language processing, and time-series predictions.
This article talks about the top 6 machine learning frameworks to use in 2021.
According to prnewswire, the global Machine Learning Market is anticipated to value USD 96.7 billion until 2025. It is also expected to register a CAGR of 43.8% over the forecasted years, 2019 to 2025.
TensorFlow – It is an end-to-end open-source machine learning platform.
Whether you are an expert or a beginner, TensorFlow is an end-to-end platform that makes it easy for you to build and deploy ML models.
It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
TensorFlow also supports an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor, and BERT.
An entire ecosystem to help you solve challenging, real-world problems with machine learning.
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
The torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio, and networking among others, and builds on top of the Lua community.
Torch is constantly evolving, and it is already used within Facebook, Google, Twitter, NYU, IDIAP, Purdue and several other companies and research labs.
Scikit-Learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.
Scikit-learn is a free software machine learning library for the Python programming language.
Scikit-learn is largely written in Python and uses NumPy extensively for high-performance linear algebra and array operations.
It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Spark ML aims to provide a uniform set of high-level APIs that help users create and tune practical machine learning pipelines.
Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms into a single pipeline, or workflow.
Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data. Spark ML adopts the SchemaRDD from Spark SQL to support a variety of data types under a unified Dataset concept.
Spark ML Estimators and Transformers use a uniform API for specifying parameters.
AWS is helping more than one hundred thousand customers accelerate their machine learning journey.
AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist, and expert practitioner.
Amazon Sage Maker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models at scale.
It removes the complexity from each step of the ML workflow so you can more easily deploy your ML use cases, anything from predictive maintenance to computer vision to predicting customer behaviours.
Simplify and accelerate AI for the entire data science team with Azure Machine Learning designer.
Azure Machine Learning studio simplifies and accelerates the process of building, testing, and deploying machine learning models for the entire data science team, from beginners to professionals.
Azure Machine Learning is a separate and modernized service that delivers a complete data science platform.
Azure Machine Learning Studio is a web portal in Azure Machine Learning that contains low-code and no-code options for project authoring and asset management.