… various machine learning algorithms. Today each and every person is using machine
learning … This paper gives an introduction to most of the popular machine learning algorithms. …

… in terms of the accuracy, speed of learning, complexity and risk … comparison with state of art
machine learning algorithms. … used supervised machine learning algorithms for classification. …

… In the following, we provide a comprehensive view of machine learning algorithms that
can be applied to enhance the intelligence and capabilities of a data-driven application. …

… Moreover, it's possible to introduce a fundamental statement: an algorithm can extrapolate
… common approaches to machine learning. Mathematical models, algorithms, and practical …

… naturally invite the use of machine learning algorithms into optimizing and automating these
… The ability of machine learning algorithms to learn from current context and generalize into …

… skills when developing the major machine learning models and when evaluating … machine
learning algorithms are learning and how they are learning them, we first build the algorithms …

… Supervised Machine Learning (ML) classification techniques, compares various supervised
learning algorithms as well as determines the most efficient classification algorithm based …

… Machine learning addresses the question of how to build computers that improve … in machine
learning has been driven both by the development of new learning algorithms and theory …

… is not necessarily involves consciousness but learning is a matter of finding statistical …
machine learning algorithms will barely resemble how human might approach a learning …

… Hence, if our learning algorithm nds a consistent hypothesis ^… Now suppose we have an
excellent learning algorithm that … the hypothesis found by the algorithm has su ciently small index…