Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

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 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.

By 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.

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 to your own predictive goals
• Understand the concepts of required statistics for Machine Learning
• Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models
• Learn reinforcement learning and its application in the field of artificial intelligence domain

About the Author

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.

Table of Contents

1. Journey from Statistics to Machine Learning
2. Parallelism of Statistics and Machine Learning
3. Logistic Regression vs. Random Forest
4. Tree-Based Machine Learning models
5. K-Nearest Neighbors & Naive Bayes
6. Support Vector Machines & Neural Networks
7. Recommendation Engines
8. Unsupervised Learning
9. Reinforcement Learning

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.