Experts believe that when a task needs to be done more than once, it should be automated. Machines are more efficient than people in doing repetitive tasks. Since it is impossible for programmers to tell machines the exact steps for every kind of repeatable task, the next best solution is to teach machines how to learn writing the steps themselves. Machine learning (ML), a subset of artificial intelligence (AI), is the study, discipline, or process of building applications or models that enable computers to learn how to write their own algorithms that get better and more accurate over time.
What is machine learning software?
Today, computers are learning or discovering on its own how to perform tasks without explicit programming. Digital assistants like Siri, Alexa, and Google Assistant are responding to text and voice commands through Natural Language Processing (NLP), a machine learning application that enables computers to understand the human language. So, what is machine learning application or software? Machine learning software is software that enables users to train computers to perform a task through massive amounts of data where computers learn to find patterns, features, and trends that allow it to make decisions and predictions.
Read also: The Best Artificial Intelligence Software
Benefits of machine learning software
Machine learning tools are helping companies like Amazon, Netflix, and Spotify give personalized recommendations to their customers. The best machine learning software has become a crucial component of business systems as companies improve their product quality or service offerings, boost revenues, improve customer relationships, and reduce costs. Machine learning software helps:
- Attract new customers and retain existing customers while improving their experience with personalization and proactive support
- Create or improve work process automation
- Increase the accuracy and maximize predictive capabilities
- Prepare organizations with the right plans and resources
- Equip businesses to adapt to changes better within the organization and the global market.
- Safeguard company assets with advanced data security.
Best Machine Learning Software & Tools for 2021
Machine learning software today learns from vast amounts of online data available. And the best machine learning tools not only collect data but can also examine contextually and classify deeply the content, sentiment, and other nuances of data sets. Here is a list of the best machine learning software in no particular order, based on reviews from multiple sites, features, customer feedback, and company ranking.
TensorFlow is an open source machine learning platform from Google. It is a software library that helps users develop and train ML models with a focus on training and deep learning of neural networks. It has a comprehensive and flexible environment of tools and libraries so researchers and data science teams can easily build ML applications. Users can build and train models with intuitive high-level APIs that are easy to iterate and debug. Colab notebooks that run directly on the browser can be used to build and run the applications.
Scikit-learn is a machine learning software library for the Python language. It is a simple and efficient tool for predictive data analysis. As open source, it is highly accessible and commercially usable. The libraries can be used for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. The algorithms can be used in a variety of applications such as spam detection, image recognition, or customer segmentation.
Splunk ML Toolkit
Splunk is a cloud-based data analytics software that can be used by companies in various industries for security, IT, or DevOps. It also has a machine learning toolkit that allows users to get actionable insights from their data. The toolkit allows them to use SPL commands to directly build, test, and operate supervised and unsupervised models. It is scalable for large data sets and can detect anomalies such as numerical and categorical outliers.
PyTorch is an open source software of machine learning libraries based on the Torch scientific computing library. It enables fast experimentation and efficient production through a user-friendly front end, distributed training, and set of machine learning tools and libraries. Other key features and capabilities are production-ready libraries, easy-to-use tools that can deploy models at scale, and optimized performance through distributed training and asynchronous execution.
Anaconda is a data science platform for data science practitioners, enterprises, and the open source community. Several editions of its product are available for different users, such as for individuals, teams, businesses, and enterprises. It can be used to scale machine learning pipeline computations, store and process data beyond the RAM of a single machine, reduce model training time, and run parallel algorithms to speed up iteration cycles.
The Apache Software Foundation has several projects for machine learning. One of them is Mahout, which allows users to produce distributed or scalable ML algorithms focused on linear algebra. It is software designed for mathematicians, statisticians, and data scientists to quickly implement their own algorithms. It runs on Apache Spark server. Another ML-related project is SINGA, a software for developing machine learning libraries that improves scalability, efficiency, and usability.
Apple Core ML
Apple Core ML is a development software that allows users to build, train, and deploy machine learning models into Apple devices such as iPhone, iPad, Apple Watch, and Mac apps. Core ML integrates pre-built ML features into apps with APIs. It can also convert models from other training libraries with its converters. Some capabilities are object detection in images, language analysis, and sound classification.
Amazon offers several AI and ML services on AWS. Its machine learning services include SageMaker, a fully managed service that lets developers and data scientists to build, train, and deploy models fast. Several SageMaker products are available, such as an integrated development environment, automated ML, training data set building, and aggregating and preparing data. ML models can be used for predictive maintenance, computer vision, or predicting customer behavior.
Google Cloud provides AI and ML products for building applications, innovating workflows, and empowering teams without ML expertise. For example, AutoML lets users train custom machine learning models with minimal effort. They can build custom models in minutes specific to their business needs. Features include object detection, image classification, video content discovery, natural language processing, and language translation.