Data science is neither magic nor rocket science, it does not create or invent any new information or facts. Data science helps us to make some sense of what’s already in front of us hidden in our data. Machine learning and its algorithms are either supervised or unsupervised as of today but the future really lies in reinforcement learning.
Backpropagation Algorithm – An important mathematical tool for making better and high accuracy predictions in machine learning. This algorithm uses supervised learning methods for training Artificial Neural Networks. The whole idea of training multi-layer perceptrons is to compute the derivatives of the error function or gradient descent with respect […]
Naive Bayes – A classification algorithm under supervised learning group based on probabilistic logic. This is one of the simplest machine learning algorithms of all times. Generative algorithms from GANs are also used as classifiers, interestingly they can do much more than categorisation though. Logistic regression is another classification […]
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.
System does self-discovery of patterns, regularities and features etc. from the input data and relations for the input data over output data. Discovering similarities and dissimilarities to forms clusters i.e. self-discovery is main target here. Since the examples given to the learner are unlabeled