Machine learning is the built-in capability in computing device including but not limited to applications in computers, mobiles or other devices, which has ability learn from data that is generated while it is being in productive use.
Machine learning uses sophisticated algorithms to “learn” from massive volumes of Big Data. The more data the algorithms can access, the more they can learn. Real-world machine learning examples are everywhere. Think of personalised product recommendations on Amazon, facial recognition on Facebook, or fastest route suggestions in Google Maps.
Machine Learning conception owes its origin in how human nervous system works.
For Example, Neural networks – aka artificial neural networks – are a type of machine learning that is loosely based on how neurons work in the human brain. They are computer programs that use multiple layers of nodes (or “neurons”) operating in parallel to learn things, recognise patterns, and make decisions.
Deep learning is a “deep” neural network that includes many layers of neurons and a huge volume of data. This advanced type of machine learning can solve complex, non-linear problems – and is responsible for AI breakthroughs such as natural language processing (NLP), personal digital assistants, and self-driving cars.
Supervised learning algorithms are trained using data that includes the correct answers. They build models that map the data to the answers – and then use these models for future processing. For example Support vector machines (SVMs) are used as a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.
Unsupervised algorithms learn from data without being given the correct answers. They use large, diverse datasets to self-improve
Machine learning algorithms provides the organisation ability to automate its business operations. An organisation can re-model itself, its products and services; making it much more efficient, extending its time and place of operations, opening new lines of business and strengthening and protecting existing lines of business.
Machine learning algorithms can prioritize and automate decision making. They can also flag opportunities and smart actions that should be taken immediately – so you can achieve the best results.
ML based devises and applications, just like humans, use existing knowledge viz historical data to decide but again as humans, uses context of ‘what is current’ or real time data to make decision. For example, a car that can automatically stop before rear-ending another vehicle.
Machine learning can analyse big, complex, and streaming data, and find insights – including predictive insights – that are beyond human capabilities. It can then trigger actions based on those insights.
With smart, machine learning-supported business processes, you can dramatically improve efficiency. Plan and forecast accurately, automate tasks, reduce costs, and even eliminate human error.
From triggering smart actions based on new opportunities and risks, to accurately predicting the results of a decision before it is made – machine learning can help you drive better business outcomes.
INFI’s ML is led by one of the top researchers in the area. A Computer Engineering alumnus and topper of IIT Kanpur – an Ivy League / Oxbridge level institute. We also collaborate with leading researchers in the area within industry and in the top academic world.
We also provide the following advantages
- Expertise in catalogue of ready to use ML applications.
- Cloud deployment & Ready to use services.
- Ability to train out-of-the-box algorithms with your data
Infi Uk Ltd,
105 Holmwood Road,
Cheam, SM2 7JS
Ph + 44 0203 129 128 5