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Machine learning is the branch of artificial intelligence which provides computer learning without explicit programming. Machine learning process is similar to data mining process. Machine learning process detect patterns through data. Machine learning plays an important role in natural language processing, image recognition, experts systems. ML can be divide into three types:
- Supervised learning – it occurs when an algorithm inferring from labeled training data and produces function. It used to analyze the training data.
- Unsupervised learning – it occurs when an algorithm inferring from unlabeled data. It is used to deform the data into any other form.
- Reinforcement learning- It performs a certain goal without any guidance.
Field of machine learning is vast and related to the computational statistics. It is used to find the meaningful anomalies and originate complex methods and algorithms.
Different Approaches of machine learning are as follows:
- Deep learning
- Bayesian learning
- Decision tree learning
- Rule based machine learning
- Representation learning
It has various application in different fields like game playing, marketing, machine perception, medical diagnosis, computational anatomy, robot locomotion, search engines, and many more.
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Machine learning goals and paradigms
- Q learning
- Learning Theory & Generalization
- Valiant’s PAC model Valiant
- Vapnik-Chervonenkis theory Abu-Mostafa
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