Reinforcement Learning (RL) – A more general form of machine learning than supervised learning or unsupervised learning. It learn from interaction with environment to achieve a goal or simply learns from reward and punishments. This learning is inspired by behaviourist phycology. Reinforcement Learning (RL) – History From the best research I got […]
Naive Bayes – A Classification algorithm under supervised learning based on probabilistic logic. This probabilistic (science of uncertainty and data) algorithm is used for making prediction in a classification fashion after predictive model gets fully tested. Naïve bayes Classifier is definitive and most important milestone to understand and to begin machine […]
Classification and Regression – Both the techniques are part of supervised machine learning. Principally both of them have one common goal i.e. to make predictions or take decision by using the past data as underlined foundations. There is one major difference as well; classification predictive output is a label […]
Unsupervised learning helps to find hidden jewel in data by grouping similar things together. Data have no target attribute. Algorithm takes training examples as the set of attributes/features alone. In this post I have summarise my whole upcoming book “Unsupervised Learning – The Unlabeled Data Treasure” in one page. […]
ML instructs an algorithm to learn for itself by analysing data. Algorithms here learn a mapping of input to output, detection of patterns or by reward. The more data it processes, the smarter the algorithm gets.