Book Review: The Hundred Page Machine Learning Book
IntroductionCan you learn everything you need to know about machine learning techniques within a book that only spans one hundred pages? This is the ambitious goal of The Hundred-Page Machine…
IntroductionCan you learn everything you need to know about machine learning techniques within a book that only spans one hundred pages? This is the ambitious goal of The Hundred-Page Machine…
IntroductionSupport Vector Machines (SVMs) are a class of supervised learning models and associated training algorithms that were founded on statistical learning theory. They were discovered in the 90s at the…
IntroductionJoint distribution, also known as joint probability distribution, calculates the likelihood of two events occurring together and at the same point in time. Joint probability is the probability that two events…
IntroductionThe new age of computing is heavily reliant on machine learning and the algorithms that power it. These algorithms process and understand large amounts of data, allowing machines to make…
What is machine learning.Machine learning is a subset of artificial intelligence that deals with the creation and study of algorithms that can learn from and make predictions on data. There…
IntroductionOverfitting and underfitting are the two most common problems encountered while doing machine learning.This article will discuss the issues we face, and how overfitting and underfitting occur. The article talks…
IntroductionAs the name would suggest, a sparse matrix is one whose elements have fewer nonzero values. Sparse matrices are encountered during machine learning and its application. It is very common…
IntroductionClassification And Regression Trees or CART for short is a term used to describe decision tree algorithms that get used for classification and regression tasks. This term was first introduced…
IntroductionMachine learning is a subfield of artificial intelligence, which is defined as the capability of a machine to simulate intelligent human behavior and to perform complex tasks in a manner…
Introduction In order to address the attribute-independence problem of the popular naive Bayes classifier, average one-dependence estimators (AODE) algorithm was developed. With very little increase in computational capabilities, it develops classifiers…