Machine Learning – an Introduction
Machine Learning (ML) holds a significant place in our daily life—be it Siri or Alexa, Facebook/Instagram friend suggestions, Gmail spam filters, traffic congestion predictions, customer support chatbots, and many more. There are different definitions of Machine Learning, for instance;Stanford defines “Machine learning is the science of getting computers to act without being explicitly programmed.” Carnegie Mellon University defines it as “The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?“
In simple terms, Machine Learning is the study of computer algorithms. An algorithm implies a process or set of commands to be followed in predictions or other problem-solving methods which is mainly performed by computers. It is the use of artificial intelligence (AI) which gives systems the intelligence to automatically learn and grow from user experience without being explicitly programmed. The fundamental aim is to let the computers learn automatically without human intrusion or assistance and modify actions accordingly. It will not be mistaken to say that the era of machines and robots has merely begun.
How Does A Machine Learn?
Previously, we mentioned that machine learns and grow from user experience, but the question is how does it learn? Machine Learning algorithm is trained using a set of training data to create a model. When a new set of data is introduced to the Machine Learning algorithm, it gives a prediction on the basis of the model. If the prediction matches a satisfactory scale of accuracy then the algorithm is deployed. In case if the prediction does not match the accuracy, then the Machine Learning algorithm is trained repeatedly by an augmented training data set until it reaches the desired scale of accuracy.
Based on the nature of learning, Machine Learning is divided into 3 types, viz;
- Supervised Learning: this isone of the most fundamental types of machine learning where the Machine Learning algorithm is given a small training dataset that acts as a teacher to train the model. To understand it in simple terms, it is when the algorithm says “Train me”.
- Unsupervised Learning: In this case, humans are not required to provide a structured dataset that is machine-readable. The model learns through observation and automatically finds structures and relationships in the dataset. This is what makes unsupervised learning algorithms very versatile. If Algorithm had to say, it would say “I am self-sufficient in learning”.
- Reinforcement learning: this type of learning happens through the trial-and-error method. Positive outputs are encouraged or ‘reinforced’, and negative outputs are discouraged or ‘punished’.
Who Is Using It?
Today almost every industry or rather every company is leveraging the capabilities of Machine Learning. Facebook, Netflix, Google, and Amazon are utilizing ML algorithms to give unique content to individual users based on their interests. Instagram is using it to find relevant leads.
Banks and other businesses in the financial industry are using machine learning to identify important insights in data for better decision making, identifying suspicious account behavior, monitoring micropayments and other payments, investment predictions, process automation, risk management, and enhancing network security. Adyen, Payoneer, PayPal, Stripe, and Skrill are some of the well-known fintech companies that invest heavily in security machine learning.
The health care sector has seen an increasingly rising number of uses of machine learning. One of the foremost ML applications is the identification and diagnosis of diseases and ailments, for drug discovery and development, in AI-driven diagnostic process like Medical Imaging Diagnosis, maintaining up-to-date health records. The advent of wearable devices and sensors that can use data to assess a patient’s health in real-time leading to improved diagnoses and treatment.
Governments are using ML to prevent crime through public surveillance devices, to track missing children and known criminals by image and voice recognition capabilities, providing better services to its citizens like generating bills, processing payments, answering queries, etc., anticipating water infrastructure, road infrastructure maintenance, replacement, and failures.
The transportation industry has seen one of the most breakthrough applications of Machine Learning like – Self-driving vehicles, the use of Sensors and cameras for traffic management, delay predictions in the aviation industry, efficient routes predictions, make parking easier, and many more.
Machine Learning has been one of the most startling technological discoveries of humankind and this write-up is just the tip of the mountain. There are endless possibilities with Machine Learning. As we said earlier, it will not be mistaken to say that the era of machines and robots has merely begun.
