Does Machine Learning Hold the Key to Successful Automation?

Sham Peeran -
machine learning
Illustration: © IoT For All

New computing technologies have facilitated a flurry of new stimuli in our daily lives. We literally interact with machine learning applications without realizing it. Everything from self-driving cars, to Netflix movie recommendations and fraud detection to shopping recommendations, embody the essence of machine learning.

Machine Learning (ML), one of the mainstays of Information Technology (IT), can be defined as a subset of Artificial Intelligence. They are a powerful set of algorithm and model which gives computers the ability to learn without being programmed. Machine learning is being extensively used across diverse industries to gain business-critical insights to solve business problems. Machine learning is not a totally new concept and has been for around 20 years.

The availability of massive amounts and varieties of data, affordable data storage, and computational processing that is cheaper and power has led to a resurgence in interest in machine learning.


Machine learning techniques such as regression, clustering, ensemble methods, transfer learning, classification, natural language processing, neural nets and deep learning, word embedding, and others are applied to massive amounts of data to retrieve valuable information. They are used in several security-related procedures such as face detection, speech recognition, image classification, signal diagnosing, etc.

Machine learning is gaining acceptance at a blistering pace across multiple industries, including healthcare, government, automotive, BFSI, and others. Most industries produce massive amounts of data and have been able to recognize the potential of machine learning technology.

By studying data in real-time, organizations can work more efficiently. Recent years have witnessed exciting advances in machine learning, which has enhanced its capabilities across wide-ranging applications. Algorithmic advances and other advances in machine learning holds the promise of supporting potentially transformative advances in a range of areas.

Industry Examples

In the healthcare sector, machine learning has led to the development of systems, which are aiding doctors in diagnosing more effectively and tailor treatments to patients.

Technology plays a very crucial in the financial services sector by identifying important insights in data. They help in the prevention of fraud and identification of opportunities through extensive data mining. Machine learning in financial services can identify clients with high-risk profiles or pinpoint warning signs of fraud using cyber surveillance.

The retail industry has been a frontrunner in the adoption of machine learning technology. Websites recommending consumers items to buy are made possible by machine learning, which analyzes the purchasing or buying pattern to do the same. In retail, machine learning is used to implement marketing campaigns, tailor customer shopping inexperience, price optimization, merchandize supply planning, and others.

The Applications of machine learning for Oil and Gas are vast and still expanding. Some of the present Applications include streamlining of oil distribution, predicting sensor failure, analyzing minerals in the ground, and identifying new energy sources.

The transportation industry is leveraging machine learning to realize patterns and trends to make routes more efficient. Machine learning serves as an important tool for logistics and other transportation companies.

Machine learning is one of the fastest-growing areas of computer science. It challenges our understanding of key concepts such as privacy and consent as it enhances our analytical capabilities.

Sham Peeran

Guest Writer
Guest Writer
Guest writers are IoT experts and enthusiasts interested in sharing their insights with the IoT industry through IoT For All.
Guest writers are IoT experts and enthusiasts interested in sharing their insights with the IoT industry through IoT For All.