Applications of machine learning are commonplace, that we don't even pay attention!

Machine learning has suddenly grabbed attention of the tech crowd, much credit goes to OpenAI's GPT-3 that can even automate creative writing! Such is the untapped potential of machine learning that is eyeing enterprise's eyeballs and their investments!

Machine learning or ML in short has applications in real life so common that we often tend to overlook! From opening your phone by facial recognition to the more complex recommender algorithms that influences your decision what you would watch or shop next, machine learning is making quite a noise for now. ML is defined as making machines learn to initiate human actions, through complex coding initiated in Python, R, C, C#, Java and so on.

Based on an algorithm's capability to learn, Machine Learning is categorized into-

• Supervised learning– Supervised learning as the name indicates requires the presence of a supervisor who acts as a teacher. Defined as a function which maps an input to an output based on example input-output pairs.

• Unsupervised learning– Used to draw inferences from datasets consisting of input data without labeled responses (supervision). Unsupervised learning algorithms include clustering, anomaly detection, neural networks, etc.

• Reinforcement learning– Defined as the training of machine learning models to make a sequence of decisions, by trial and error.

Success Behind a Machine Learning Roadmap

Measuring the success behind a Machine Learning roadmap depends on the choice of algorithm, cost of running an ML algorithm, quality of data chosen, and the desired level of accuracy chosen.

Based on the parameters, Machine Learning finds its application's into-

Healthcare– To make accurate predictions. About 10 percent of patient deaths is caused by the errors in diagnosis. Predictive analytics can help to diagnose ailments at an early stage and reduce patient deaths. Spotting complications related to tumours. Doctors at Addenbrooke's Hospital in Cambridge collaborated with the tech giant Microsoft to train machine learning algorithms to find brain tumours in 3-D magnetic-resonance-imaging (MRI) scans.

Manufacturing– Supervised learning is deployed to predict future desired or undesired events even before they happen. A system, which is often supervised by a human engineer or scientist, is automatically triggered by the relevant input data, captured through IoT sensors. The software programme can easily recognize inconsistencies and anomalies in any machine component that may have taken ages for the human to discover by just looking at the raw data.

Retail– Retail is increasingly becoming more personalised with the application of ML technologies.  Retailers, manufacturers, marketing, and advertising platforms collaborate to given a personalised experience to end-users helping them to understand a product/ content online before a purchase to develop better brand loyalty.

Retail will no longer focus only on selling products, but go a step higher and deliver a personalised experience.

Real Estate and Construction – ML is still untapped in the construction and real estate domains. The lower cost availability of the supervised 360-degree cameras add to depth measurements, and point to cloud data, AI and more. Many construction majors are increasingly deploying 360-degree drone cameras to document, report, and communicate data in new and intelligent ways.

Supply Chain Logistics- Unsupervised machine learning helps supply chain managers to optimise inventory and find most suited suppliers to keep their business optimised. An increasing number of businesses today are showing interest in the applications of machine learning, from resource planning, risk mitigation, delivering customer satisfaction, determining transactional costs and transportation costs to improve insights and enhance performance.

There is nothing like a perfect machine learning roadmap! Data Scientists, Data Analysts who are ML experts constantly tweak and alter their algorithms for the desired accuracy. Challenges do arise during this process ranging from building data pipelines, determining data ownership to choosing the right model, and zeroing on the desired accuracy levels.