9 Most Popular Machine Learning Tools You Should Know in 2022
Today’s machine learning (ML) is different from the machine learning of the past. There is a change in work as the new computing techniques are introduced (not the architecture it’s based on, i.e., pattern recognition learning).
Even though many ML algorithms have been here and there for a long time, the ability to perform complex mathematical calculations on extensive data and provide faster and more accurate results is a recent development.
The past few years have been a good year for freedom of information, as big tech companies like Microsoft, Google, Amazon, Facebook, and even Baidu open-sourced several ML frameworks.
Working in an ML environment using the right tools can be very useful for developers trying to create a productive algorithm that takes advantage of its power. This year, we’ve rounded up some of the best machine learning tools and resources to help you understand and seamlessly integrate the power of ML into your everyday tasks.
1. Scikit-learn
What started as the Google Summer of Code is now one of the most popular Python machine learning libraries. It provides a good selection of algorithms for classification, regression, clustering, model selection, and preprocessing.
Scikit-learn uses Python to C compiler, also known as Cython, to achieve fast performance. And it works well with Python’s numerical (NumPy) and scientific (SciPy) SciPy libraries.
Scikit-learn is probably the best choice among simple machine learning frameworks if you enjoy learning machine learning with Python. However, if one is working on a large-scale project, we recommend that you consider other tools.
2. Tensorflow
Developed by the Google brain Team, TensorFlow is arguably the best open source library for complex computing and system getting to know at a mass scale. It uses Python to provide a convenient API for constructing applications with the framework and implements all matrix multiplications in C++ to make calculations fast. This machine learning tool is also helpful for data analysis purpose.
TensorFlow can train and run deep neural networks for simulations based on partial differential equations, herbal language processing, word embedding, picture reputation, handwritten digit classification, and recurrent neural networks.
Let’s say you want to debug and gain perception into TensorFlow packages. If so, its “non-stop execution” mode permits you to study and edit all chart operations for my part, rather than growing the whole chart as a single object and checking it all of a sudden.
3. Pytorch
PyTorch is an open-source ML library for Python based on Caffe2 and Torch. It is primarily developed by Facebook and is mainly used for applications such as natural language processing.
Its two main features are Tensor computation with high GPU acceleration and Deep Neural Networks designed for maximum accuracy and flexibility.
It is not a binding of Python into a monolithic C++ framework. PyTorch is developed to integrate with Python and used with popular packages and libraries such as Numba and Cython.
Perhaps the best choice for projects that need to be launched in a short time.
The Microsoft Cognitive Toolkit uses directed graphs to express neural networks as a series of computational steps. This open-source framework is developed using sophisticated algorithms (core libraries are written in C++) and production readers to work with large datasets reliably.
It allows developers to implement and connect familiar types of models, including recurrent networks, convolutional neural networks, and feedforward deep neural networks. CNTK modules can handle sparse or multidimensional dense data from C++, Python, and BrainScript.
Read: ML.NET | Microsoft’s Open Source Machine Learning Framework
In addition, the framework can implement stochastic gradient descent learning in parallel across multiple GPUs and machines and embed massive-scale models in GPU memory.
It handles several neural network tasks faster and has extensive APIs.
5. Keras
Released in 2015, the Keras open-source neural network library aims to be modular, user-friendly, and extensible. In 2017, Google started supporting Keras in the core TensorFlow library.
It has several predefined layers, organized into categories: kernel, locally connected, embedding, normalization, noise, convolution, pooling, and advanced activation. There is an API for writing layers also.
Each layer performs a specific task. They usually pass most computationally intensive operations to a backend such as Microsoft Cognitive Toolkit or TensorFlow.
Not only standard neural networks, but Keras also keep up recurrent and convolutional networks. It provides 7 common sample data for deep learning and 10 well-known models pre-trained against ImageNet.
6. Apache Mahout
Apache Mahout is a distributed linear algebra framework for building and implementing scalable machine learning algorithms focused primarily on clustering, classification, and batch collaborative filtering. It is implemented on top of Apache Hadoop using the MapReduce paradigm.
Mahout includes matrix and vector libraries and comes with support for complementary Naive Bayes and Distributed Naive Bayes classifier implementations. It also has distributed fitness functions for evolutionary programming. Several companies such as Twitter, Yahoo, LinkedIn, Foursquare, and Facebook already use this framework internally. Yahoo uses Mahout for pattern mining, while Twitter uses it for user interest modeling.
The best choice for data scientists, statisticians, and mathematicians.
7. IBM Watson Studio:
IBM Watson Machine Learning is a hands-on ML framework that uses data to build ML and deep learning models for production. It is best suited for building ML applications through API connections. It helps developers perform the two main ML activities training and scoring. Easy model creation and training can be done faster, accelerating the implementation of ML workflows in your daily business activities. Built and scaled trusted AI on any cloud with AI lifecycle automation for models. This tool also used for data analysis for machine learning.
Key properties:
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Enables data scientists to manage and run AI models
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It syncs well with other open-source frameworks like TensorFlow etc.
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It automates the AI ??lifecycle and speeds up the entire process
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It supports NLP, visual recognition, and Blockchain processes
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Simplifies the deployment of ML models
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Easily combine optical data science with open-source libraries.
8. Microsoft Azure Machine Learning:
Microsoft’s Azure Machine Learning is an enterprise ML service for building and deploying AI and ML models efficiently and faster. It enables users to create critical ML models with good quality and efficiency. It solidifies value to the market with interoperable features, integrated tools, and machine learning operations (MLOps). ML models can easily be deployed with a single click via MLO.
Its key features are
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Well-defined pre-trained models
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Free trial version
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Useful for developers with limited coding experience
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An extensive set of product accessories
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Flexible and easy building interface
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A fast and straightforward implementation of web services
9. Amazon Lex:
Amazon Lex is a secure, simple, and comprehensive ML framework and fully managed service for solving serious learning problems such as speech and text recognition, language perception, etc. It has strong integration with AWS Lambda for backend business logic integration. Chatbots with a conversational ML-based interface can be directly implemented on IoT, chat platforms, and mobile devices. With Lex, bots can be created, monitored, and published quickly and efficiently.
Key properties:
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Quality speech recognition and natural language processing
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Easily manage context across conversations
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Multi-turn conversations and dialogues
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One-click deployment for multiple platforms
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Stream natural conversations
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Seamless integration with other technology frameworks
As we have seen above, the main tools and techniques of machine learning, it is up to the organization which one to choose. The decision rests in the hands of the stakeholders based on various parameters such as project deadlines, costing, availability of skilled resources, ease of use, etc.
The world of machine learning services and solutions has revolutionized the world, and these popular ML tools act as suitable catalysts to reap the maximum benefits!
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