Machine learning is no longer regarded as a theoretical area of Artificial Intelligence research. Machine learning has become commercially viable because of advances in data storage and retrieval innovations as well as increased speed and performance of modern processors.
With these advancements, systems are able to analyze vastly larger datasets than ever before. Hardware capabilities alone haven’t brought about renewed interest in and commercial success from the field.
Statistical computing language libraries, systems, and associated developer communities have enabled unprecedented growth.
Machine Learning Impacts Everyday Life
Machine learning is commonly used for detecting similarities and anomalies, depending on the application, from a variety of data types.
Fraud Detection – Machine learning has completely replaced rule-based fraud detection methods which tended to have high false positive rates. Machine learning can detect a wide range of fraud—including potentially devastating financial fraud—and at a rapid pace. Fraud detection with machine learning is possible because algorithms can be trained with fraudulent account activity, then the resulting model can be used to recognize similar patterns on future activity, even highly sophisticated schemes human reviewers might otherwise miss.
Medical Image and Data Analysis – Machine learning models have been used in medical image analysis and to determine possible cancer patient outcomes based on probable risk markers. Due to the deeply complex issues surrounding the use of machine learning in medicine, machine learning is still widely regarded as experimental or supplemental to human diagnosis and review of patient data. (Read also: Top 20 AI Use Cases: Artificial Intelligence in Healthcare.)
Chatbots & Digital Assistants – Machine learning enables a range of chatbot types that can handle different kinds of queries while interacting with humans in a conversational format. Chatbots can assist with account-based tasks for financial institutions, student services and human resources at universities, customer service for retailers, and much more. Digital assistants such as Alexa, Google Assistant, Cortana, and Siri use machine learning for conversational responses.
Cybersecurity – Machine learning is used in cybersecurity to seek out anomalies in network traffic or human behavior patterns. A multitude of devices and users generate a large amount of data. Machine learning is used to detect potential threats from network traffic patterns or behaviors. Machine learning can sort through all the data points faster than a human reviewer could.
E-commerce – In addition to chatbots and product suggestions, online retailers can also use machine learning to update product listings, manage product reviews, automate CRM data collection (and so much more). (Read also: Utilizing Visual Artificial Intelligence for Ecommerce Monetization.)
5 Programming Languages for Machine Learning
Applying machine learning to solve a particular problem requires a team. Real world applications for machine learning are built with cooperation from engineers, scientists and programmers as they come together to find the best solutions for a given problem.
As the field has grown, some clear scientific programming language preferences have emerged from the community. System and programming language preference largely depends on developer professional experience and project requirements.
Python is an open source, general purpose, programming language. It is regarded as an easy to read and easy to learn high-level language. Python’s growing popularity in the scientific computing community is largely due to the language’s ease of use, extensive user base, and available machine learning libraries. Python is also "platform agnostic" so it can run on a range of operating systems. (Read also: Why is Python so popular for machine learning?)
Machine Learning Python (Mlpy) – The Mlpy module can be used for supervised and unsupervised learning methods. Mlpy algorithms include regression, classification, clustering, and more.
TensorFlow – TensorFlow is a versatile platform for deep learning and neural networks. It is used for natural language processing, image recognition, and more.
NumPy – NumPy is a numerical computing library that contains multidimensional array and matrix data structures. NumPy provides efficient calculations using arrays and matrices for high-level mathematical tasks.
The R was designed for statistical analysis and visualization. R is an open-source alternative to a similar statistical computing language called S. In addition to its array of statistical techniques, R is also favored for its high-quality visualization output (e.g. print-ready graphics). R is highly extensible via packages. R works with other languages as well. C, C++, and Fortran can be called at runtime for computation-heavy tasks. (Read also: Data Science Debate Between R and Python.)
randomForest – randomForest is a package for classification and regression algorithms that implements Breiman’s randomForest algorithm.
rpart – rpart is a package used for recursive partitioning, classification, and survival trees.
DataExplorer – The DataExplorer package automates data exploration tasks for predictive modelling.
Brain.js – Brain.js is a modular, easy-to-use, library used for neural networks. Brain.js uses GPU-accelerated processing in the browser.
TensorFlow.js – TensorFlow.js is a deep learning and neural network library that can be used in the browser. TensorFlow.js can be used to define, train, and deploy machine learning models from the browser.
Math.js – Math.js is a flexible math library that can be used with different data types like complex numbers, fractions, matrices, and big numbers.
Dynet – Dynet is a dynamic neural network toolkit for C++ and is used for natural language processing, machine translation, and more. The toolkit can be run on GPU or CPU and is well suited for dynamic structures that change with every training instance.
Caffe – Caffe is a deep learning framework. Caffe is commonly used for machine vision, speech, and multimedia applications. This framework can run on GPU or CPU without hand-coding. Caffe has been deployed on large-scale industrial applications processing vision and voice recognition.
OpenNN – Open Neural Network (OpenNN) is a sophisticated open source neural network library for C++. OpenNN is suitable for regression, classification, forecasting, and association. OpenNN has been used for business intelligence, engineering, healthcare, and more.
A large number of enterprise applications are already running on Java. The language is often chosen for projects within organizations utilizing it for other applications. Java is scalable. It’s suitable for larger, complex applications. As with the languages mentioned above, Java also has a number of machine learning libraries. (Read also: Why is Java Preferred to Other Languages as a Building Block?)
Java-ML – Java Machine Learning Libraries (Java-ML) offers a large collection of machine learning algorithms for feature selection, data processing, clustering, and more. Java-ML does not include a graphical user interface and is primarily used by engineers and programmers.
Apache Mahout – Apache Mahout is a distributed linear algebra and mathematically expressive ScalaDSL for data scientists, mathematicians, and statisticians to create their own algorithms. Mahout is used for classification, clustering, and collaborative filtering.
Apache Spark – Apache Spark is a scalable unified analytics engine used for large-scale data processing framework that can distribute data processing tasks across multiple systems. Spark can be used for multiclass classification algorithms, clustering, collaborative filtering, regression, and much more.
Weka – Waikato Environment for Knowledge Analysis (Weka) is an open source machine learning software package used in teaching, research, and industrial applications. It can perform common machine learning tasks (such as classification, regression, and clustering) and includes built-in help and teaching guides.
Machine learning is commercially viable and continues to spurn researchers to find answers to more complex questions. Further advancements will continue as long as teams of researchers, scientists, and programmers work together to solve complex problems.