Most Popular Machine Learning Tools

Today machine learning technology is implemented across various business sectors to coordinate processes based on predicted outcomes. If you are looking to improve your organization’s efficiency, consider leveraging machine learning tools and machine learning certification and training. These platforms can help your team build machine-learning models that will generate meaningful insights. In turn, this can lead to smarter business decisions and better outcomes across the organization. In our increasingly digital world, data is quite literally everywhere. Every click, every swipe, every video, and every word can be converted into very abundant (and, in the right hands, lucrative) data. But there isn’t enough time in the world for us humans to sift through all of this data, understand it, and use it to its full advantage. That’s why we have machine learning, giving computers the ability to not only automate data analysis but do it in such a way that it can “learn” through experiences and context rather than simple coding — much in the same way we humans learn.

12 Popular Machine Learning Tools to Know

Let us have a look at the most popular Machine Learning Tools.

Apache Mahout

Developed by the Apache Software Foundation, Mahout is an open-source library of machine learning algorithms, implemented on top of Apache Hadoop. It is most commonly used by mathematicians, data scientists, and statisticians to quickly find meaningful patterns in very large data sets. In practice, it is especially useful in building intelligent applications that can learn from user behavior and make recommendations accordingly.

AWS Machine Learning

AWS Machine Learning offers a variety of tools designed to help developers discover patterns in user data through algorithms, construct mathematical models based on those patterns, and generate predictions from those models. Some of its free product offerings include Amazon Rekognition, which identifies objects, people, text, and activities in images and video; and Amazon SageMaker, which helps developers and data scientists build, train and deploy machine learning models for any use case.

BigML

BigML provides machine learning algorithms that allow users to load their own data sets, build and share their models, train and evaluate their models and generate new predictions either singularly or in a batch. And all of the predictive models created on BigML come with interactive visualizations and explainability features that make them more interpretable. Today, the platform is used across a variety of industries, from aerospace to healthcare, according to the company.

Google Colab

Google’s Colab is a cloud service that helps developers build machine learning applications using the libraries of PyTorch, TensorFlow, Keras and OpenCV — some of which will be further discussed later in this piece. It allows users to combine this code with rich text, images, HTML and more into a single document in order to build and train machine learning models. These models can then be stored on a Google Drive, shared, and edited by others.

Google Cloud AutoML

Based on the tech giant’s state-of-the-art transfer learning and neural architecture search technology, Google Cloud AutoML is a collection of machine learning products that helps developers train high-quality models for whatever they need them for, even if they have limited machine learning experience. The tool allows users to evaluate, enhance and deploy their models, as well as train. They can also generate predictions on their trained models and securely store whatever data they need in the cloud.

IBM Watson Studio

IBM’s Watson is among the most familiar players in not just machine learning, but also cognitive computing and artificial intelligence in general since it won a game of Jeopardy! in 2011 against two human champions. Today, the IBM Watson Studio helps developers put their machine learning and deep learning models into production, offering tools for data analysis and visualization, as well as cleaning and shaping data.

Microsoft Azure Machine Learning

Azure Machine Learning offers everything developers need to build, test and deploy their machine learning models, placing an emphasis on security. Its collaborative, drag-and-drop design takes developers throughout the entire machine learning process and comes with features for data exploration preparation, model training, and development, model validation, as well as continuous monitoring and management of the model. Plus, the tool requires no programming — rather, it visually connects the data sets and modules to help users build their predictive analysis model.

OpenNN

Short for Open Neural Networks Library, OpenNN is a software library that implements neural networks, a key area of deep machine learning research. It is written in C++ programming language and the entire library can be downloaded for free from GitHub or SourceForge.

PyTorch

PyTorch is an open-source tool that helps with deep learning and machine learning model development. The platform offers tensor computing, neural networks, and a host of machine learning libraries and tools. PyTorch also has additional wrappers — PyTorch Lightning and PyTorch Ignite — both of which are meant primarily to expand on research capabilities and diminish the need for redundant code.

Scikit-learn

Scikit-learn is among the most used libraries for machine learning. It is Python-based and contains an array of tools for machine learning and statistical modeling, including classification, regression, and model selecting. Because scikit-learn’s documentation is known for being detailed and easily readable, both beginners and experts alike are able to unwrap the code and gain deeper insight into their models. And because it is an open-source library with an active community, it is a go-to place to ask questions and learn more about machine learning.

Shogun

Shogun is a free, open-source machine-learning software library that offers numerous algorithms and data structures for machine-learning problems. It also offers interfaces for many languages, including Python, R, Java, Octave, and Ruby. This is one of the more “underrated” libraries for machine learning, according to Emmett Boudreau, a popular contributor to the Towards Data Science blog — likely due to its smaller user base and maintainer list. But Boudreau said the Shogun library is more established language-wise, which leads to more accessibility both cross-platform and in different applications.

TensorFlow

Initially developed by Google, TensorFlow is an open-source machine learning framework, offering a variety of tools, libraries, and resources that allow users to build, train and deploy their own machine learning models. It supports a wide range of solutions, including natural language processing, computer vision, predictive machine learning, and reinforcement learning. While TensorFlow does offer some pre-built models for simpler solutions, it mostly requires developers to work closely with a given model’s code, which means they can achieve full control in training the model from scratch. TensorFlow also has a deep learning API for Keras, called tf. keras.