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Machine learning algorithms make predictions or decisions based on data. These learning algorithms can be embedded within applications to provide automated, artificial intelligence (AI) features. A connection to a data source is necessary for the algorithm to learn and adapt over time. There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific machine learning algorithms, such as association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others.
These algorithms may be developed with supervised learning or unsupervised learning. Supervised learning consists of training an algorithm to determine a pattern of inference by feeding it consistent data to produce a repeated, general output. Human training is necessary for this type of learning. Unsupervised algorithms independently reach an output and are a feature of deep learning algorithms. Reinforcement learning is the final form of machine learning, which consists of algorithms that understand how to react based on their situation or environment.
End users of intelligent applications may not be aware that an everyday software tool is utilizing a machine learning algorithm to provide automation of some kind. Additionally, machine learning solutions for businesses may come in a machine learning as a service (MLaaS) model.
To qualify for inclusion in the Machine Learning category, a product must:
Scikit-learn is a software machine learning library for the Python programming language that has a various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
machine learning support vector machine (SVMs), and support vector regression (SVRs) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.
Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business with full support for Python and R. Machine Learning Server meets the needs of all constituents of the process – from data engineers and data scientists to line-of-business programmers and IT professionals. It offers a choice of languages and features algorithmic innovation that brings the best of open-source and pro
GoLearn is a 'batteries included' machine learning library for Go that implements the scikit-learn interface of Fit/Predict, to easily swap out estimators for trial and error it includes helper functions for data, like cross validation, and train and test splitting.
MLlib is Spark's machine learning (ML) library that make practical machine learning scalable and easy it provides ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering, feature extraction, transformation, dimensionality reduction, and selection, tools for constructing, evaluating, and tuning ML Pipelines, saving and load algorithms, models, and Pipelines and linear algebra, statistics, data handling, etc.
Weka is a machine learning algorithms for data mining tasks that can either be applied directly to a dataset or called from own Java code, it contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization and well-suited for developing new machine learning schemes.
XGBoost is an optimized distributed gradient boosting library that is efficient, flexible and portable, it implements machine learning algorithms under the Gradient Boosting framework and provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
Crab as known as scikits.recommender is a Python framework for building recommender engines that integrate with the world of scientific Python packages (numpy, scipy, matplotlib), provide a rich set of components from which user can construct a customized recommender system from a set of algorithms and be usable in various contexts: ** science and engineering ** .
Apache Mahout is a software that build an environment for quickly creating scalable performant machine learning applications, it provides three major features: A simple and extensible programming environment and framework for building scalable algorithms, A wide variety of premade algorithms for Scala + Apache Spark, H2O, Apache Flink and Samsara, a vector math experimentation environment with R-like syntax which works at scale
Pattern Recognition and Machine Learning is a Matlab implementation of the algorithms.
The Xilinx ML Suite enables developers to optimize and deploy accelerated ML inference. It provides support for many common machine learning frameworks such as Caffe, MxNet and Tensorflow as well as Python and RESTful APIs.
Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point and it creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.
At Eggplant we empower organizations to create amazing digital experiences. We help businesses to test, monitor and analyze their end-to-end customer experience and continuously improve their business outcomes. Companies worldwide use Eggplant to surpass competitors, boost productivity, and delight customers. How? By dramatically enhancing the quality, responsiveness, and performance of their software applications across different interfaces, platforms, browsers, and devices - including mobile
SHARK is a fast, modular, feature-rich open-source C++ machine learning library that provides methods for linear and nonlinear optimization, kernel-based learning algorithms, neural networks, and various other machine learning techniques and is compatible with Windows, Solaris, MacOS X, and Linux.
Dlib Machine Learning is a tool that contains a wide range of machine learning algorithms, designed to be highly modular, quick to execute, and simple to use via a clean and modern C++ API and used in a wide range of applications including robotics, embedded devices, mobile phones, and large high performance computing environments.
Bolt is a discriminative learning of linear predictors (e.g. SVM or Logistic Regression) that uses fast online learning algorithms to aimed large-scale, high-dimensional and sparse machine-learning problems. In particular, problems encountered in information retrieval and natural language processing.
Machine learning software automates tasks for users by leveraging an algorithm to produce an output. These solutions are typically embedded into various platforms and have use cases across a wide variety of industries. Machine learning solutions improve the speed and accuracy of desired outputs by constantly refining them as the application digests more training data.
Machine learning software improves processes and introduces efficiency to multiple industries, ranging from financial services to agriculture. Machine learning applications include process automation, customer service, security risk identification, and contextual collaboration. Notably, end users of machine learning-powered applications do not interact with the algorithm directly. Rather, machine learning powers the backend of the artificial intelligence (AI) that users interact with. Some prime examples of this include chatbots software and automated insurance claims management software.
There are three main types of machine learning software: supervised, unsupervised, and reinforcement. These refer to the type of algorithm on which the application is built. The type of machine learning doesn’t generally affect the end product that customers will use. For example, whether a virtual assistant is built using supervised learning or unsupervised learning matters little to the companies that employ it to deal with customers. Companies care more about the potential impact that deploying a well-made virtual assistant will bring to their business model.
This model of machine learning refers to the idea of training the machine or model with a specific dataset until it can perform the desired tasks, like identifying an image of a certain type. The teacher has complete control over what the model or machine learns because they are the ones inputting the information. This means that the teacher can steer the model exactly in the direction of the desired outcome.
Unsupervised learning refers to the algorithm or model that is dispatched with the mission to search through datasets to find structures or patterns on its own. However, unsupervised learning is unable to label those discovered patterns or structures. The most they can do is distinguish patterns and structures according to perceived differences.
With this type of machine learning, the model learns by interacting with its environment and giving answers based on what it encounters. The model gains points for supplying correct answers and loses points for giving incorrect ones. Through this incentivizing method, the model trains itself. The reinforcement learning model will learn through its interactions and ultimately improve itself.
Deep learning algorithms, a subset of machine learning algorithms are those that specifically use artificial neural network software, which are models based on the neural networks in the human brain that react and adapt to information, learning to make decisions based on that information.
Core features within machine learning software help users improve their applications, allowing for them to transform their data and derive insights from it in the following ways:
Data: Connection to third-party data sources is the key to the success of a machine learning application. To function and learn properly, the algorithm must be fed large amounts of data. Once the algorithm has digested this data and learned the proper answers to typically asked queries, it can provide users with an increasingly accurate answer set.
Often, machine learning applications offer developers sample datasets to build their applications and train their algorithms. These prebuilt datasets are crucial for developing well-trained applications because the algorithm needs to see a ton of data before it’s ready to make correct decisions and give correct answers. In addition, some solutions will include data enrichment capabilities, like annotating, categorizing, and enriching datasets.
Algorithms: The most important feature of any machine learning offering is the algorithm. It is the foundation off of which everything else is based. Solutions either provide prebuilt algorithms or allow developers to build their own in the application.
Machine learning software is useful in many different contexts and industries. For example, AI-powered applications typically use machine learning algorithms on the backend to provide end users with answers to queries.
Application development: Machine learning software drives the development of AI applications that streamline processes, identify risks, and improve effectiveness.
Efficiency: Machine learning-powered applications are constantly improving because of the recognition of their value and need to stay competitive in industries in which they are used. They also increase the efficiency of repeatable tasks. A prime example of this can be seen in eDiscovery, where machine learning has created massive leaps in the efficiency with which legal documents are looked through and relevant ones are identified.
Risk reduction: Risk reduction is one of the largest use cases in financial services for machine learning applications. Machine learning-powered AI applications identify potential risks and automatically flag them based on historical data of past risky behaviors. This eliminates the need for manual identification of risks, which is prone to human error. Machine learning-driven risk reduction is useful in the insurance, finance, and regulation industries, among others.
Machine learning software has applications across nearly every industry. Some of the industries that benefit from machine learning applications include financial services, cybersecurity, recruiting, customer service, energy, and regulation industries.
Marketing: Machine learning-powered marketing applications help marketers identify content trends, shape content strategy, and personalize marketing content. Marketing-specific algorithms segment customer bases, predict customer behavior based on past behavior and customer demographics, identify high potential prospects, and more.
Finance: Financial services institutions are increasing their use of machine learning-powered applications to stay competitive with others in the industry who are doing the same. Through robotic process automation (RPA) applications, which are typically powered by machine learning algorithms, financial services companies are improving the efficiency and effectiveness of departments, including fraud detection, anti-money laundering, and more. However, the departments in which these applications are most effective are ones in which there is a great deal of data to manage and a lot of repeatable tasks that require little creative thinking. Some examples may include trawling through thousands of insurance claims and identifying ones that have a high potential to be fraudulent. The process is similar, and the machine learning algorithm can digest the data to get to the desired outcome much quicker.
Cybersecurity: Machine learning algorithms are being deployed in security applications to better identify threats and automatically deal with them. The adaptive nature of certain security-specific algorithms allows applications to tackle evolving threats more easily.
Alternatives to machine learning software that can replace it either partially or completely include:
Natural language processing (NLP) software: Businesses focused on language-based use cases (e.g., examining large swaths of review data in order to better understand the reviewers’ sentiment) can also look to NLP solutions, such as natural language understanding software, for solutions specifically geared toward this type of data. Use cases include finding insights and relationships in text, identifying the language of the text, and extracting key phrases from a text.
Image recognition software: For computer vision or image recognition, companies can adopt image recognition software. With these tools, they can enhance their applications with features such as image detection, face recognition, image search, and more.
Related solutions that can be used together with machine learning software include:
Chatbots software: Businesses looking for an off-the-shelf conservational AI solution can leverage chatbots. Tools specifically geared toward chatbot creation helps companies use chatbots off the shelf, with little to no development or coding experience necessary.
Bot platforms software: Companies looking to build their own chatbot can benefit from bot platforms, which are tools used to build and deploy interactive chatbots. These platforms provide development tools such as frameworks and API toolsets for customizable bot creation.
Software solutions can come with their own set of challenges.
Automation pushback: One of the biggest potential issues with machine learning-powered applications lies in the removal of humans from processes. This is particularly problematic when looking at emerging technologies like self-driving cars. By completely removing humans from the product development lifecycle, machines are given the power to decide in life or death situations.
Data quality: With any deployment of AI, data quality is key. As such, businesses must develop a strategy around data preparation, making sure there are no duplicate records, missing fields, or mismatched data. A deployment without this crucial step can result in faulty outputs and questionable predictions.
Data security: Companies must consider security options to ensure the correct users see the correct data. They must also have security options that allow administrators to assign verified users different levels of access to the platform.
Pattern recognition can help businesses across industries. Effective and efficient predictions can help these businesses make data-informed decisions, such as dynamic pricing based upon a range of data points.
Retail: An e-commerce site can leverage a machine learning API to create rich, personalized experiences for every user.
Finance: A bank can use this software to improve their security capabilities by identifying potential problems, such as fraud, early on.
Entertainment: Media organizations are able to leverage recommendation algorithms to serve their customers with relevant and related content. With this enhancement, businesses can continue to capture the attention of their viewers.
If a company is just starting out and looking to purchase their first machine learning software, wherever they are in the buying process, g2.com can help select the best machine learning software for them.
Taking a holistic overview of the business and identifying pain points can help the team create a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a machine learning platform.
Create a long list
From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after the demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.
Create a short list
From the long list of vendors, it is advisable to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.
To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.
Choose a selection team
Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.
Prices on a company's pricing page are not always fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.
After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.
Machine learning software is generally available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will usually lack features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, either unlimited or capped at a certain number of hours per billing cycle.
Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.
Businesses decide to deploy machine learning software with the goal of deriving some degree of an ROI. As they are looking to recoup their losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is sometimes tiered depending on the company size.
More users will typically translate into more licenses, which means more money. Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform.
The adoption of machine learning is related to a broader trend around automation.RPA is driving an increased interest in the machine learning space because machine learning enables RPA. RPA is gaining in popularity across multiple verticals, being particularly useful in industries heavy on data entry, like financial services because of its ability to process data and increase efficiency.
Human vs. machine
With the adoption of machine learning and the automation of repetitive tasks, businesses are able to deploy their human workforce to more creative projects. For example, if a machine learning algorithm automatically displays personalized advertisements, the human marketing team can work on producing creative material.