Machine learning is the ability of machines, i.e., computers to learn and improve from their past experience or data without being explicitly programmed. In fact, it is a subset of AI (Artificial Learning) and Deep Learning is a subset of ML (Machine Learning). Deep Learning is a subset of ML which makes computation of multilayer neural network feasible. It uses statistical methods to enable machines to learn from past behavior or data to predict the outcomes . Artificial Intelligence is a capability of a machine to mimic intelligent human behavior.
Currently, Machine Learning and Artificial Intelligence are covering the majority of technology being developed.
Types of Machine Learning
Supervised Learning: Supervised Learning learns under supervision in which machines are trained using labelled data i.e., some data is already tagged with correct answers.
Unsupervised Learning: Unsupervised Learning is a complement of supervised learning which works on only unlabeled data where no target or output variable is involved. Basically it detects output based on inherit characteristics of input data.
Reinforced Learning: Reinforced learning is a ML training method where model is trained to make a sequence of decisions. In other words, the AI faces a game-like scenario.
Applications
- Social Media(Facebook) : ‘Automatic Friend Tagging Suggestions’ where it uses face detection and image recognition to automatically find the face of person which matches it’s database and hence suggests us to tag that person.
- Google Translate : GNMT(Google Neural Machine Translation) is a Neural Machine Learning which uses NLP (Natural Language Processing) to provide the most accurate translation of any sentence or words.
- Banking Sector : It is undoubtedly used in banking sector to predict the credit worthiness of credit card holders.
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