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    Machine learning software (MLS) is a tool for creating advanced computer applications that employ massive datasets and complex algorithms to train itself, apply knowledge, and develop its capability to predict.

    A subset of artificial intelligence (AI), machine learning is useful for a variety of data-reliant computing tasks, including speech recognition, facial recognition, object recognition, translation, and predictive analytics.

    How does machine learning work?

    Machine learning (ML) can teach itself, learn data, and set its own rules to help people make better decisions based on data. It trains itself by feeding on data inputs to come up with outputs—pattern discovery, predictive analysis, or improved learning.

    With machine learning software, it is easier to build models for predicting data trends, assessing the accuracy of predictions, and optimizing processes. The computer program processes and sorts a massive amount of information and identifies data patterns that are humanly impossible to detect.   

    Types of machine learning

    Supervised or Task-Driven

    Supervised, or task-driven, machine learning involves classification and regression algorithms, which use previous data to make predictions in a supervised environment. The machine follows human-directed inputs, connecting them with the outputs.

    A common example is its application for filtering spam emails. Based on past data, the software decides which emails are spam and which ones to send to the inbox.

    Unsupervised or Data-Driven

    Unsupervised, or data-driven, machine learning employs clustering and association algorithms, where the machine is left to analyze and discover data patterns from fed inputs. This type of ML is used in social media and content delivery services.

    The computer application recognizes the identities of objects or persons in photographs, finds patterns of human behavior based on their browsing history and other online activities, and then makes recommendations. 

    Reinforcement or Learning from Errors

    The machine receives data input continuously to improve knowledge in a trial-and-error manner, thereby increasing efficiency. It takes inspiration from how humans learn new information, reinforcing favorable outputs and disincentivizing unfavorable ones.

    This type of ML is used in self-driving cars, where the machine learns new information about the road and improves its decision-making processes.

    Key features of MLS

    Machine learning tools have these key features in common:

    • Data mining, data analysis, and business intelligence
    • Pattern recognition and predictive analytics
    • Algorithms for classification, regression, clustering, association, model selection, dimensionality reduction, visualization, and building neural networks
    • Training tools and libraries for building models
    • Compatibility with various programming languages

    MLS use cases

    MLS use cases span across industries, from manufacturing to healthcare to government. The most common business use of MLS is developing AI-driven applications in the areas of security and surveillance, speech recognition, computer vision, targeted marketing, customer service, analytics, recommendation engines, and social media.

    eWeek reports on how Shopify leveraged machine learning software for anomaly detection and forecasting.


    Advantages of MLS

    • Provides access to machine learning training tools and libraries
    • Helps build models to automate a range of business processes
    • Simplifies the creation of tools for data mining, processing, and analysis
    • MLS it easy for AI operations teams to build, train, and deploy ML-powered applications

    Some examples of MLS

    • Azure Machine Learning: It makes building, training, and deploying machine learning models easy with pre-trained models that users with limited coding backgrounds can quickly adapt to. 
    • Scikit-learn: Built for Python, Scikit-learn is a free-to-use MLS ideal for data mining and analytics, offering algorithms for classification, clustering, regression, model selection, dimensional reduction, and pre-processing. 
    • PyTorch: With Lua as its computing framework and scripting language, PyTorch uses Python’s machine learning library. Its algorithms are designed for building neural networks. 
    • KNIME: Designed for data mining, analytics, reporting, and integration, KNIME is best for business intelligence, financial analysis, and customer relationship management (CRM). It is also useful for integrating different programming languages, such as Python, Java, R, and C++
    • Accord.Net: offers free machine learning libraries for image, audio, and signal processing as well as algorithms for numerical optimization, statistics, and artificial neural networks. 
    • Shogun: Providing support for various programming languages like Python, R, Octave, Ruby, and Java, Shogun is easy to use and processes ultra-large datasets. It comes with libraries for learning and research. 
    • Other machine learning software include IBM Machine Learning, Neural Designer,, Anaconda, TensorFlow, Google Colab, and Amazon Machine Learning.

    Read more about AI development tools.