Machine Learning Algorithms: Theory and Practice

Machine learning is a branch of artificial intelligence that enable computer systems to learn explicitly from example, data, and experience. Through enhancement, computers can perform specific tasks intelligently without human intervention. Machine learning systems can carry out complex analysis by learning or training from data. Currently, there are exciting improvements in machine learning, which have raised its capabilities across many business application platforms and other corridors. By employing big data availability, has enabled machine learning systems to be trained using big data platforms, while increasing computer processing capabilities to analyze data explicitly. Within the domain itself, there have been various algorithmic advances, which have resulted in the utilization of machine learning algorithms and subsequently utilized by large companies: Google, Amazon, Microsoft, Netflix, and so on. This book provides an intuitive illustration of machine learning algorithms, their theories and implementations, and various techniques in supervised, unsupervised, or semi-supervised learning algorithms including some sample source codes for user's visualization.

Autorentext

The author's professional career started as a mathematics and computer programming teacher for over ten years at the Milton Margai Technical University. He is a researcher, data scientist, and software developer at both local and international levels. He attained the following qualifications: H.T.C, B.Sc.(Hons.), M.Sc., M.Eng., and Ph.D. (ongoing).