English | ISBN: 1788295757 | 2017 | EPUB | 311 pages | 12 MB

Key Features
Learn about the statistics behind powerful predictive models with p-value, ANOVA, F-statistics.
Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.
Master the statistical aspect of machine learning with the help of this example-rich guide in R & Python.
Book Description
Complex statistics in machine learning worries a lot of developers. Knowing statistics helps in building 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. You will see real-world examples that discuss the statistical side of machine learning and make you comfortable with it. You will come across programs for performing tasks such as model, parameters fitting, regression, classification, density collection, working with vectors, matrices, and more.By the end of the book, you will understand concepts of required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problems.

What you will learn
Understanding Statistical & Machine learning fundamentals necessary to build models
Understanding major differences & parallels between statistics way of solving problem & machine learning way of solving problem
Know how to prepare data and "feed" the models by using the appropriate machine learning algorithms from the adequate R & Python packages
Analyze the results and tune the model appropriately to his or her own predictive goals
Understand concepts of required statistics for Machine Learning
Draw parallels between statistics and machine learning
Understand each component of machine learning models and see impact of changing them