Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so.
By Harshad Mehendale
One has often wondered about the marvels of technology. Its speed, precision, uniformity and ease in accessibility has transcended from age-old PC’s to the contemporary hand held devices. However what if we told you that this progression was merely a gradual and expected phenomenon and we are yet to witness more fascinating capabilities of technology!
Till date, computers performed according to programs and applications that were created specifically for conducting a particular task. To cite a simple illustration, we are able copy images from the web and edit it to our taste by using platforms such as MS Picture Manager or Paint or Adobe Photo Shop. What if computers, like humans, began learning from experience? And this has already begun to take shape…
Machine Learning is the dawn of an exciting new era of info and computer science wherein computers can figure out how to perform important tasks by generalizing from examples.
What is Machine Learning?
As more data becomes available, more ambitious problems can be tackled. Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so.
Machine learning is linked to artificial intelligence, the development of computers with skills that traditionally would have required human intelligence, such as decision-making and visual perception. It is the part of artificial intelligence that actually works. You can use it to train computers to do things that are impossible to program in advance. Search engines like Google and Bing, Facebook and Apple’s photo tagging application and Gmail’s spam filtering are everyday examples of machine learning at work. The fundamental goal of machine learning is to generalize beyond the examples in the training set.
Two facets of mechanization should be acknowledged when considering machine learning in broad terms. Firstly, it is intended that the classification and prediction tasks can be accomplished by a suitably programmed computing machine. Secondly, it is intended that the creation of the classifier should itself be highly mechanized, and should not involve too much human input.
Machine Learning vs. Artificial Learning
One often believes machine learning to be synonymous with artificial intelligence but it isn’t so. Artificial intelligence is a broad term referring to computers that are capable of essentially coming up with solutions to problems on their own. The information needed to get to the solution is coded and AI uses the data to come up with a solution.
On the other hand Machine Learning takes the process one step further. Machine learning is capable of generalizing information from large data sets, and then detects and extrapolates patterns in order to apply that information to new solutions and actions.
Machine Learning and Artificial Intelligence are highly inter-dependent fields that they need each other to analyze and perform activities.
Machine Learning Apps
Machine learning is playing a significant role in transforming a wide variety of industries. Given below are areas where some of the most reputed institutions are trying to employ machine learning tools.
Employing Machine Learning in Business
The dynamic nature of the market is a major hurdle for senior management of organizations across verticals. The diversifying trends and evolving consumer preferences are compelling organizations to rely on technology to understand and analyze market situations more accurately.
Today’s cutting-edge technology already allows businesses not only to look at their historical data but also to predict behavior or outcomes in the future. Machine learning is a critical tool used for gaining actionable insight into ever increasing amounts of data. The most common application of machine learning tools is to make predictions and find solutions for problems in a business:
Making customized recommendations for customers
Anticipating the future performance of employees
Forecasting customer loyalty
The Machine Learning algorithms draw their insight by comparing new cases to a large database of similar cases from the past. With the right mix of technical skill and human judgment, machine learning can be a useful new tool for decision-makers trying to make sense of the inherent problems of big data. However one must realize that no matter what fresh insights computers have to offer, only human beings are capable of responding to the essential questions, such as what are the critical concerns of an organization and what is the most feasible manner to address the concerns.
Machine Learning in Cloud Computing
Machine Learning is a fully managed, on-demand, pay-as-you-go and easy to use service provided by prominent cloud providers like Amazon Web Services, Microsoft Azure and Google Cloud Platform. The cloud-based Machine Learning service gives business a chance to get started with Machine Learning and make valuable decisions.
AWS: Amazon ML is a highly scalable Machine Learning service that provides visualization tools and wizards that guide you through the process of creating ML models. On the basis of ML models, Amazon ML APIs can generate billions of predictions for your applications. Amazon ML also helps fine-tune the interpretation of the predictions.
MS Azure: Microsoft Azure ML is a powerful cloud-based predictive analytics service. It helps building predictive models based on existing data using Machine Learning Studio in order to forecast future behaviors, outcomes, and trends. Azure ML Studio service provides a development environment to build, test, and deploy predictive analytics solutions. It is collaborative visual development experience in the Azure.
Google: Google Prediction API provides pattern-matching and machine learning capabilities. It helps analyze data to add features in your application like spam detection, message routing decision, customer sentiment analysis etc. The Prediction API works on the two most important aspects viz. create an appropriate question for the API and provide appropriate data that can be used by API to answer that question.
The author is Consultant of Blue Star Infotech. Views are personal.