Book Image

Practical Machine Learning Cookbook

By : Atul Tripathi
Book Image

Practical Machine Learning Cookbook

By: Atul Tripathi

Overview of this book

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.
Table of Contents (21 chapters)
Practical Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
14
Case Study - Forecast of Electricity Consumption

Introduction


The Markov chain: A sequence  of trials of an experiment is a Markov chain if the outcome of each experiment is one of the set of discrete states, and the outcome of the experiment is dependent only on the present state and not of any of the past states. The probability of changing from one state to another state is represented as. It is called a transition probability. The transition probability matrix is an n × n matrix such that each element of the matrix is non-negative and each row of the matrix sums to one.

Continuous time Markov chains: Continuous-time Markov chains can be labeled as transition systems augmented with rates that have discrete states. The states have continuous time-steps and the delays are exponentially distributed. Continuous-time Markov chains are suited to model reliability models, control systems, biological pathways, chemical reactions, and so on.

Monte Carlo simulations: Monte Carlo simulation  is a stochastic simulation of system behavior. The...