Statistics for Machine Learning -: Essential statistical concepts for exploring predictive analytics and machine learning using Python and R

Build Supervised, Unsupervised & Reinforcement Learning models with real-world examples
About This Book
* Perform inferential statistical analysis on your data using Python and R
* Explore forecasting techniques to integrate time series data with larger machine learning models
* Build powerful predictive models and machine learning applications with real-world example
Who This Book Is For
Developers, Software Engineers, Data Science Aspirants, Machine Learning Enthusiasts who wants to develop strong grounding in statistics and build dynamic career in machine learning. Experienced Machine Learning Engineers, Jr. Data Scientist, Data Analyst, Big Data Engineers can also find this book useful to refresh their statistical knowledge. All you need is some programming experience in Python and R to practically enjoy the role of statistics in your ML applications.
What You Will Learn
* Learn Statistical and Machine Learning fundamentals necessary to build models
* Explore different Statistical techniques for Machine Learning using R and Python packages
* Learn reinforcement learning and its application in artificial intelligence domain
* Explore Probabilistic Graphical Models to create powerful Artificial Intelligence
* Learn Time Series Prediction Using Recurrent Neural Networks
* Understand Bayesian methods to estimate uncertainty in prediction
In Detail
Statistics for Machine Learning, Second Edition provides the practical understanding of the underlying concepts those work as real engines to make a powerful predictive model and effective machine learning.
This book teaches you to perform complex statistical computations required for Machine Learning using popular packages of Python and R programming. the book equips you with complete statistics that powers supervised learning and unsupervised learning on your large datasets. You will learn Bayesian statistics, time series modeling, Probabilistic Graphical Models and many more. You will also work around real-world examples that discuss the statistical working in detail which will guide you in implementing equations, modeling distributions, probabilities and statistics rules to address the business problem right way.
By the end of the book, you will build solid statistical skills required for your machine learning and predictive analytics project irrespective of the nature of your business