A Demanding Environment For Machine Learning

By Shailendra Choudhary, Vice President and Head-IT, Interarch Building Products Pvt. Ltd.

Shailendra Choudhary, Vice President and Head-IT, Interarch Building Products Pvt. Ltd.Established in 1984, Interarch is one of leading turnkey PreEngineered Steel Construction Solution providers in India with integrated facilities for design, manufacture, logistics, supply and project execution capabilities for pre- engineered steel buildings.

In today’s demanding economic environment, organizations that can deploy machine learning. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an out while updating outputs as new becomes available.

“Machine learning focuses on the development of computer programs that can access data and use it learn for themselves”

The processes involved in machine learning are similar to shat of data mining and predictive modelling. Both require searching through data to look for patterns and adjusting program actions accordingly. Many people are familiar with machine learning from shopping on the internet and being served ads related to their purchase. This happens because recommendation enquires use machine learning to personalize online ad delivery in almost real time. Beyond personalized marketing, other common machine learning use cases include fraud detection, spam filtering, network security threat detection, predictive maintenance and building news feeds.

How Machine Learning Works

Machine learning algorithms are often categorized as supervised or unsupervised. Supervised algorithms require a data scientist or data analyst with machine learning skills to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during algorithm training. Data scientists determine which variables, or features, the model should analyze and use to develop predictions. Once training is complete, the algorithm will apply what was learned to new data.

Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions. Unsupervised learning algorithms - also called neural networks - are used for more complex processing tasks than supervised learning systems, including image recognition, speech-to-text and natural language generation. These neural networks work by combing through millions of examples of training data and automatically identifying often subtle correlations between many variables. Once trained, the algorithm can use its bank of associations to interpret new data. These algorithms have only become feasible in the age of big data, as they require massive amounts of training data.

Example of Machine Learning

Today machine learning is being used in a wide range of applications. One of the most well-known examples is Facebook’s News Feed. The News Feed uses machine learning to personalize each member’s feed. If a member frequently stops scrolling to read or like a particular friend’s posts, the News will start to show more of that friend’s activity earlier in the feed. Behind the screens, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use those patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend’s posts, that new data will be include in the data set and the News Feed will adjust accordingly.

Machine is also entering in Enterprise Applications. Customer Relationship Management (CRM) systems use learning models to analyze email and prompt sales team members to respond to the most important messages first. And analytics vendors use machine learning in their Software to help users automatically identify potentially important data points. Human Resource (HR) systems use learning models to identify characteristics of effective employees and rely on this knowledge to find the best applicants for open positions. Machine learning also plays an important role in self-driving cars.

The Future of Machine Learning

Machine learning platforms are among enterprise technology's most competitive realms, with most major vendors, including Amazon, Google, Microsoft and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, model building, training and application deployment. As machine learning continues to increase in importance to business operations and AI becomes ever more practical in enterprise settings, the machine learning platform wars will only intensify.

Machine learning represents the future or present for some of advanced analytics. When you or the analysts have completed the data gathering and high level analytics, maybe it’s time to consider machine learning rather than brute-forcing advanced solutions through tools like Excel. Machine learning provides fast, accurate and flexible solutions which may represent the next step in analytics for your business.

Why Machine Learning Is Important

Machine learning has several very practical applications that drive the kind of real business results such as time and money savings that have the potential to dramatically impact the future of your organization.

Machine learning has made dramatic improvements in the past few years, but we are still very far from reaching human performance. Many times, the machine needs the assistance of human to complete its task.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

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