Major results in mathematical statistics theory such as the Binomial Theorem, the Laplace distribution, the Gaussian or normal distribution, the logarithmic distribution, the Poisson distribution, the Bernoulli distribution, Bayes theorem, and many others were created by giants in mathematics before the 1800s. The early form of central limit theorem was discovered in the 1800s.In the early 1900s till early 1930s, statistical theories including rigorous formulations of probability distributions by Pearson, the design of experiments and the maximum likelihood method by Fisher, concepts of testing by Neyman and others, were born. The extreme value theory was formulated in the 1920s. The practice of statistics had become rigorous within the framework of probability theory. The latter could have been from the time of Pascal and Fermat, though in modern times, it could have been based on the measure-theoretic approach by Kolmogorov. The march of statistical applications continuing into modern rigorous and analytical forms would not have been possible without the great efforts and tirelessness of generations of brilliant minds in these fields.Machine learning is a new wave of approach that might have begun in the late 1950s but picked up momentum only in the 1990s. It is a scientific approach that is partly statistical in perspectives and partly engineering in spirit, driven by explosions in data quantities or big data and the commensurate increase in the capacity of computer machines to manage and scrutinize these data. It has come to be somewhat hinged directly with the more general domain of data science and artificial intelligence.This book is an introduction to machine learning using Python programming language with applications in finance and business. The coverage of the book is contained in the Introduction section of this book. There will be a strong emphasis on financial and business applications, as well as fundamental information on corporate reporting data and market fundamental factors. The book also contains detailed examples of applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own.
