THIRD EDITION OF Machine Learning IN BUSINESSThis book is for business executives and students who want to learn about the tools used in Machine learning.
Chapter 10 is concern THI.
It discusses how the algorithms introduced in the book can be used for sentiment analysis, language translation, and information retrieval.
Chapter 9 covers natural language processing.
It covers Q-Learning and deep Q-learning, and discusses applications.
Chapter 8 explains reinforcement Learning using two games as examples.
Chapter 7 covers autoencoders, variational autoencoders, generative adversarial networks, convolutional neural networks, and recurrent neural networks.
It includes a discussion of the gradient descent algorithm and stopping rules.
Chapter 6 is devoted to neural networks.
Chapter 5, explains how the SVM approach can be used for both linear and non-linear classification as well as for the prediction of a continuous variable.
It includes a discussion of the naive Bayes classifier, random forests, and other ensemble methods.
Chapter 4 covers decision trees.
It covers regularization using Ridge, Lasso, and Elastic Net.
Chapter 3 explains linear and logistic regression.
It also covers principal components analysis.
It explains the k-means algorithm and alternative approaches to clustering.
Chapter 2 is devoted to unsupervised learning.
It also explains the issues involved in cleaning Data and covers Bayes\' theorem.
It explains the role of the training Data set, the validation Data set, and the test Data set.
The opening chapter reviews different types of Machine Learning models.
A complete set of PowerPoint slides that can be used by instructors is also on the website.
Data, worksheets, and Python code for the examples is on the author\'s website.
These include assessing the risk of a country for international investment, predicting the value of real estate, classifying retail loans as acceptable or unacceptable, understanding the behavior of interest rates, using neural networks to understand volatility surface movements, and using reinforcement Learning for optimal trade execution.
There are many illustrative examples throughout the book.
The focus is on business applications.
The book explains the most popular algorithms clearly and succinctly without using calculus or matrix/vector algebra.
He has added new case studies and new material on the applications of neural networks.
In creating the third edition, John Hull has continued to improve his material.
THIRD EDITION OF Machine Learning IN BUSINESSThis book is for business executives and students who want to learn about the tools used in Machine learning
