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Machine learning (ML) is an integral sub-field of AI or Artificial Intelligence that applies statistical, logical and mathematical techniques in order for computers to progressively learn data processing without exclusive programming. AI plays a vital role in powering innovation.
Just to clarify the difference: The field of data mining has acquired most of its motivation from machine learning. However, data mining is executed by an individual, within a certain situation, on a targeted set of data and with predefined goals and objectives. The ideal objective of this individual is to make the most of the trend recognition techniques that are developed in ML. There are two primary objectives of doing this – either to gain insights in a domain with a small knowledge base or to predict future trends and patterns more efficiently. Some of the standard techniques of data mining include cluster analysis, regression trees and neural networks.
On the other hand, Machine Learning is a set of algorithms that help applications in accurately processing data and predicting output progressively, without having to be explicitly programmed for executing the same. The basic concept of ML is about building systems that can learn from the data and use the learnings to predict or classify outcomes for new data points.
Machine learning plays an important role in data analytics and is applied to situations, where it is too complex to otherwise quantify relationships among various variables. This enables analysts to detect new patterns and repeatedly produce dependable insights with the help of such advanced data processing techniques.
How Machine Learning works
It is broadly classified into two categories:
- Supervised machine learning algorithms and
- Unsupervised data processing algorithms
Supervised machine learning algorithms are trained using “labelled” data, hence they are called supervised. It is like parents try to help kids learn what behaviour is “good” and what is “bad”. “Good” and “Bad” are labels attached to the historical data points related to behaviour. Once the training phase is accomplished, the algorithm will apply then it to the new data inputs to either do predictions or classifications.
The unsupervised machine learning algorithms do not require any such training or supervision. They are useful when the spread of data itself is useful in deriving insights. The few common examples of unsupervised learning are Clustering (Segmentation), Anomaly Detection, etc.
There also are techniques, which can be classified as “Semi-supervised” learning.
Applications of Machine Learning:
Machine learning is applied to every industry that works with large data sets. Effective learning systems provide insights that help businesses make efficient decisions and outdo their competitors. Here are some of the industry use cases to give you an idea of where ML is applied:
The technological enhancements of the twentieth century have given us the boon of wearable devices and sensors that help us assess a patient’s health or activity in real time. Such technology also has the potential to improve healthcare research and help experts assess and analyse enormous amounts of research and medical data to get valuable insights contributing to improved diagnosis, treatment and efficiently expedite life sciences research.
The application of ML or machine learning in the early stages of drug discovery has a high potential in the initial screening of drug compounds and to predict their success rate based on the respective parameters and factors.
iPlexus is a good example of efficiently applied machine learning that helps generate intelligence and insights across pre-clinical, clinical, regulatory and commercial phases of a drug development to discover most relevant knowledge and new patterns.
The banking and financial industry applies machine learning for various purposes, two of which stand out the most:
- Acquiring reliable insights from data for risk management and prevention of fraud e.g. by identifying high-risk profiles.
- Improving the efficiency in terms of compliance, and reducing the risk of misconduct. Regulatory compliance in the financial services sector has tremendously benefited by the application of ML e.g. automating the tracking regulations and amendments. RegI maps the typical workflow of a regulatory specialist by automatically sectioning, summarizing, and assessing the areas of impact of a regulatory document and its various versions. It continuously monitors for changes and updates of different financial regulatory bodies, proposals, initiatives as well as rules and regulations consultations and discussion papers issued by the relevant authorities.
Ever used an e-commerce website to order online? Such e-commerce websites efficiently deploy machine learning algorithms to analyse your buying patterns and push the other most relevant items customized and unique to every user. This ability to acquire data, assess and analyse it to personalise a user’s shopping experience is certainly a game changer in the retail industry. Recommendation Engines are one such example.
The global transportation infrastructure relies on making transportation efficient by predicting problems and solving the same before they arise. Machine learning in the transportation industry help plays a key role in identifying such trends and patterns to improve predictive capabilities.
Machine Learning is a very narrow field. But in only a few years, it will become part of nearly every software application and will bring benefit to every industry and our daily lives. It replaces manual, repeatable processes. Thus, it is a must for us to comprehend the concept of ML and apply it for the betterment of humanity. This is exactly what Innoplexus does.