Machine Learning is a major driving force behind the intelligent analytics packages that are increasingly essential to modern business. Machine Learning algorithms or platforms learn iteratively from the data sets and can find new insights without being explicitly programmed where to look. This enables computers to create increasingly "smart" insights, predictions and decisions. Business ML solutions focus on creating new & higher value out of BI to improve efficiency and effectiveness. Read the full software guide...
Machine learning is a type of algorithm or computer programming that enables software systems and applications to become more efficient and accurate in predicting potential outcomes.
As the term suggests, software solutions with machine learning capabilities are programmed to learn from user behavior, actions, and patterns to make a calculated assessment of potential outcomes based on gathered data.
The fundamental principle of machine learning is to create algorithms that are able to receive and analyze input data by way of statistical analysis in order to predict outcomes while maintaining the ability to adjust outputs based on new incoming data.
Some of the core processes associated with machine learning are somewhat similar to that of predictive modeling and data mining. Both of those processes involve sifting through data to search for patterns, while at the same time adapting program actions to suit newly available information.
Machine learning has become quite prevalent in today’s digital environment, although the majority of internet users may not be aware of it. People who shop online often or go on social media are exposed to machine learning. Every ad or suggestion/recommendation they encounter is a product of machine learning algorithms.
There are several types of machine learning solutions. Of late, machine learning has been categorized into three main types: supervised learning, reinforcement learning, and deep learning.
Supervised Machine Learning
Supervised learning is a category of machine learning that involves feeding a computer system a multitude of examples of a given item and allowing it to calculate and analyze the examples to look for specific patterns and similarities.
Based on the thousands, millions, or even billions of examples, the computer system will then be able to extrapolate patterns and similarities for other examples of the provided item that have not yet been discovered.
One good example of this type of machine learning is Google’s image search. Ask Google to search for an image similar to your example and it can pretty much come up with search results that are close to or exactly the same as the given image.
Deep Machine Learning
Deep learning is a category of machine learning which requires computer systems to iteratively perform complex calculations in order to discover patterns and similarities on their own. This type of machine learning involves complex calculations conducted by the computer system in order to scrutinize items or examples for the tiniest details so that they will be able to predict outcomes more accurately.
Even though deep learning involves a plethora of sophisticated calculations, you need to keep in mind that all of this is happening in a split second, such that users won’t even notice it. As more and more data is entered into the computer system with deep learning capabilities, it becomes smarter and more efficient in predicting possible outcomes.
Reinforcement Machine Learning
Reinforcement learning is a category of machine learning that involves teaching the computer system if it has made a correct assumption/decision or not based on user input. With enough iteration, a reinforcement learning system will eventually have the capability to learn and predict potential outcomes or make the right decisions.
A good example of reinforcement learning is answering questions posed by an application or any other system where the user is required to choose between two answers—normally it’s a yes or no question.
When you’re on social media, sometimes the app will ask you if an ad it showed was helpful or not, your answer will be taken in by the machine learning system and use it as a basis for future decisions. Your answer to that particular query will essentially dictate whether you will be seeing similar ads in the future or not.
When trying to choose the right machine learning solution for your needs, there are two basic questions you first need to ask yourself: why and when. Why do you need a machine learning solution, and when should you use it?
To answer those two questions, you also have to figure out what kind of problems machine learning does solve. Machine learning systems are useful and effective, there’s no doubt about that. But, this doesn’t necessarily mean everyone should start using one.
Always remember that machine learning solutions are designed to help users solve specific problems that are either too complex or difficult or too time-consuming for any individual to perform using traditional programming solutions.
When choosing machine learning as a strategic implementation and performance enhancement solution, you should also remember that machine learning solutions come in a wide variety of packages, platforms, and suites. More often than not, machine learning algorithms are incorporated into software solutions designed to perform specific tasks and functions.
If you’re in the sales and marketing industry, for instance, you might want to look into CRM solutions and marketing automation platforms that utilize machine learning to deliver reliable results. Marketing automation solutions benefit greatly from machine learning as it effectively enhances the original system’s efficiency and reliability in providing actionable outcomes.
You may also want to consider the features and capabilities of the solution, as well as the type of machine learning algorithm used, as discussed above. Is the system using supervised learning, reinforcement learning, deep learning, or a combination of those types?
So why is machine learning important? What are its benefits? The importance of machine learning and the benefits it offers can be compared to that of predictive modeling and data mining.
When you’re a business dealing with growing volumes and varieties of available data, it’s important to have a reliable and efficient tool that will allow you to sift through the information you have to find the most relevant data you can use to improve your business.
Machine learning technology helps businesses, organizations, and even individual users transform their processes to become more streamlined, efficient, and frankly convenient. Users will be able to find relevant data quicker and easier. Furthermore, with applications utilizing machine learning algorithms, customers will benefit from what that system has to offer.
|A/B Testing||Run split tests for websites, emails, ads and more by serving different versions of the content to different users.|
|Analytics||Analyze and gain insights from data including web traffic, campaign conversions, sensoric output and more.|
|API||Application Programming Interfaces (APIs) are programmatic intersections with external products or platforms that allow for custom integrations with your own solutions or other solutions you are using.|
|Big Data||Support for very large data sets often used to analyze customer behavior, patterns and trends.|
|Cloud Deployment||Available via the cloud rather than on-premise.|
|Dashboard||Dashboards are digital interfaces commonly used to visualise data or give quick access to important features and functions of online platforms. They often serve as an overview gateway in software applications.|
|Data Export||Exporting functionality can be used to streamline the migration of data sets and information across systems, platforms or applications.|
|Data Import||Importing functionality allows you to use data sets from other systems or platforms to cut down on data entry requirements or to more easily migrate records from similar applications you have used in the past.|
|Data Mining||Data Mining is a term commonly used in conjuction with Big Data. It refers to the analysis of large data sets to identify patterns, trends or extract other new information.|
|Data Visualization||Data visualization features render a visual interpretation of data sets through the use of charts, infographics and other visual cues generally in form of a reporting dashboard.|
|External Integrations||Integrations with other software products or platforms to improve efficiency and compatibility across systems.|
|Local Deployment||Available on premise rather than via the cloud.|
|Multi-User||Supports more than just one user account and generally allows for collaboration with colleagues.|
|Optimized Search Processing||Improved search capabilities, allowing for shorter times associated with processing queries across large data sets.|
|SAP Integration||Integrates with common SAP services.|
|Sentiment Analysis||Analyze the markets opinion towards a particular topic or product using computational processes.|
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