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A beginner-friendly crash course to statistics utilizing Python with an eye to preparing students for further study in machine learning Key Features A quick introduction to Python for statistics Hands-on projects for guided practice Instant access to PDFs, Python codes, exercises, and references on the publisher's website at no extra cost Book Description Data and statistics are the core subjects of Machine Learning (ML). The reality is that the average programmer may be tempted to view statistics with disinterest. But if you want to exploit the incredible power of ML, you need a thorough understanding of statistics. The reason is that a machine learning professional develops intelligent and fast algorithms that learn from data. This Statistics Crash Course for Beginners presents you with an easy way of learning statistics fast. Contrary to popular belief, statistics is no longer the exclusive domain of math PhDs. It's true that statistics deals with numbers and percentages. Hence, the subject can be very dry and boring. This book, however, transforms statistics into a fun subject. Frequentist and Bayesian statistics are two statistical techniques that interpret the concept of probability in different ways. Bayesian statistics was first introduced by Thomas Bayes in the 1770s. Bayesian statistics has been instrumental in the design of high-end algorithms that make accurate predictions. So, even after 250 years, the interest in Bayesian statistics has not faded. In fact, it has accelerated tremendously. Frequentist statistics is just as important as Bayesian statistics. In the statistical universe, Frequentist statistics is the most popular inferential technique. In fact, it's the first school of thought you come across when you enter the statistics world. By the end of this course, you will have built a solid foundation in statistical theory and practice that will prepare you for further study in machine learning and a career in programming. The code bundle for this