Machine Learning (ML) is an important subset of Artificial Intelligence (AI). It deals in making computers learn to do something that they are not programmed to do, through discovering repetitions, patterns and insights in real-time data and information. ML today forms an integral component of almost every kind of software being used, whether on the web / cloud or offline. Couple of popular examples of ML in action are the stories, the posts and the people suggested to you on social media feed, as well as the dynamically evolving word suggestion system available on your smart phone, as you type into it anytime, as also every time you instruct Alexa to do something (play the music station of your choice, increase room temperature, search for your favorite recipe etc.).
ML is a continuous process of system learning and algorithmic evolution that leads computers to deliver their tasks with more accurate and precise results. At a higher level, it is the ability of computer systems and networks to adapt to new data generated in an independent, continuous and iterative manner. The ML process begins with the transfer of input training data into the chosen algorithm. Then, new data is fed into the algorithm to test whether it works correctly. Then, the prediction and the actual results are checked and compared. In case the result is not as expected, the algorithm is re-trained multiple times till the desired output is obtained.
Coming to the types of ML, supervised learning involves the use of known input data for training the computers, resulting into monitored training and execution. Once the algorithm is formulated and tested based on known data, unknown data can then be used to elicit fresh response. In contrast, unsupervised learning involves the use of input data that is unknown – no one has looked at it before – neither the humans nor the machines. Once the unknown data is fed and starts training the machine, it looks for patterns and elicits the desired response. Next, in the reinforcement form of learning, the algorithm reads data through a process of trial and error, and then determines which action results in higher rewards. Of course, a prior framework needs to be defined to specify the nature and the form of reward.
As such, the core process of ML comprises of 2 stages – learning, and prediction. Learning involves the input of training data into the algorithm, which is then processed by the computer to learn in a supervised / unsupervised / reinforced manner, followed by error analysis – a step that ensures gradually higher precision and accuracy of the result. The actual prediction stage comprises of the interaction of the model / algorithm with the new data – which leads to the occurrence of prediction inside the machine, followed by the generation and the display of the predicted data.
Some popular use cases of ML include, but are not limited to, self-driving cars, cyber crime detection, and online recommendation engines. Some other applications are internet search results, real-time ad display on the web pages / mobile devices, e-mail spam filter mechanism, detection of network intrusion, and image / pattern recognition. All of these applications use the technology of ML to analyze massive volumes of data and predict an output value or piece of information. As such, ML sees vast business usage in data quality and management, modelling and process flow building, interactive data analytics and visualization of results for better interpretation, inter-comparison of ML models to quickly spot the best one for application, as well as model evaluation and deployment, as also integrated end-to-end platforms for automation and streamlining of day-to-day business operations (examples of such platforms are ERPs and CRMs, including CRM-on-cloud).
In totality, machine learning makes for a great way to move from the analog way of working and living to the digital way of experiencing and delight. ML combines the wisdom of the old with the smartness of the new to dish out a recipe of technology that’s been tasted by many, and loved by one and all.