Machine learning (ML) is an application of Artificial Intelligence (AI) that provides system the ability to automatically learn and improve from its past experiences using statistical techniques without being explicitly programmed. This focuses on the development of computer programs that can access data and use it to learn for them. Some of the things provided to us by Machine Learning are self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.
The name Machine Learning was coined by Arthur Samuel in 1959. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Machine Learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible.
The more formal definition of algorithms studied in the Machine Learning was given by Tom M. Mitchell which says, “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P it its performance at tasks in T, as measured by P, improves with experience E.”
Machine Learning tasks are typically classified into following broad categories:
- Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that mapsinputs to outputs. As special cases, the input signal can be only partially available, or restricted to special feedback.
- Semi-supervised learning:The computer is given only an incomplete training signal: training set with some (often many) of the target outputs missing.
- Active learning: The computer can only obtain training labels for a limited set of instances (based on a budget), and also has to optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling.
- Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
- Reinforcement learning: Data are given only as feedback to the program’s actions in a dynamic environment, such as driving a vehicleor playing a game against an opponent.
Any person or firm has their own field of use of Machine Learning and there are many Machine Learning Consulting companies which can provide one with best program needed for any particular business.
The technical part is for the ones who are going to want to learn about the Machine Learning but for a person or a private or non-private firm, what are the applications of Machine Learning and why it is important?
The applications of Machine Learning are:
- In Classification: Inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more classes. Example is Spam Filtering, where the inputs are email messages and the classes are ‘spam’ and ‘not spam’.
- In Agriculture: For observing, measuring, and responding to inter and intra-field variability in crops.
- In Automated theorem proving: A great help for researchers and students. Based on observation and learning from originally proved theorems.
- In Bioinformatics: For understanding biological data and developing methods and specific medicines.
- In Cheminformatics: To solve a range of problems in field of chemistry. Used in pharmaceutical companies and research centers in the process of drug discovery.
- In Telecommunication: In channeling the signals to the user and providing the best service.
Other examples are Computer Vision, Detecting Credit-card frauds, Marketing, Automated Medical Diagnosis, Online Advertising, Search Engines, Financial marketing analysis, Speech and handwriting recognition, User behavior analytics etc.