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\u003cp\u003e\u003cb\u003eBuild Machine Learning models with a sound statistical understanding.\u003c/b\u003e\u003c/p\u003e\u003ch2\u003eAbout This Book\u003c/h2\u003e\u003cul\u003e\u003cli\u003eLearn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.\u003c/li\u003e\u003cli\u003eImplement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.\u003c/li\u003e\u003cli\u003eMaster the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.\u003c/li\u003e\u003c/ul\u003e\u003ch2\u003eWho This Book Is For\u003c/h2\u003e\u003cp\u003eThis book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.\u003c/p\u003e\u003ch2\u003eWhat You Will Learn\u003c/h2\u003e\u003cul\u003e\u003cli\u003eUnderstand the Statistical and Machine Learning fundamentals necessary to build models\u003c/li\u003e\u003cli\u003eUnderstand the major differences and parallels between the statistical way and the Machine Learning way to solve problems\u003c/li\u003e\u003cli\u003eLearn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages\u003c/li\u003e\u003cli\u003eAnalyze the results and tune the model appropriately to your own predictive goals\u003c/li\u003e\u003cli\u003eUnderstand the concepts of required statistics for Machine Learning\u003c/li\u003e\u003cli\u003eIntroduce yourself to necessary fundamentals required for building supervised \u0026 unsupervised deep learning models\u003c/li\u003e\u003cli\u003eLearn reinforcement learning and its application in the field of artificial intelligence domain\u003c/li\u003e\u003c/ul\u003e\u003ch2\u003eIn Detail\u003c/h2\u003e\u003cp\u003eComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.\u003c/p\u003e\u003cp\u003eBy the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.\u003c/p\u003e\u003ch2\u003eStyle and approach\u003c/h2\u003e\u003cp\u003eThis practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.\u003c/p\u003e |
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