Build Machine Learning models with a sound statistical understanding.

Key Features:

• Learn about the statistics behind powerful predictive models with p-value, ANOVA, and 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 to R and Python.

Book Description:

Complex 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 the complex statistical computations that are required for machine learning. You will gain information on the 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 familiarize yourself with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more.

By the end of the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.

What You Will Learn:

• Understand the statistical and machine learning fundamentals necessary to
• build models
• Understand the major differences and parallels between the statistical way and the machine learning way to solve problems
• Learn how to prepare data and feed models by using the appropriate machine learning algorithms from the more-than-adequate R and Python packages
• Analyze the results and tune the model appropriately