Ivan Pastor Sanz - Machine Learning with R Quick Start Guide: A beginners guide to implementing machine learning techniques from scratch using R 3.5

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Ivn Pastor Sanz is a lead data scientist and machine learning enthusiast with extensive experience in finance, risk management, and credit risk modeling. Ivn has always endeavored to find solutions to make banking more comprehensible, accessible, and fair. Thus, in his thesis to obtain his PhD in economics, Ivn tried to identify the origins of the 2008 financial crisis and suggest ways to avoid a similar crisis in the future.

Sahaj Pathak has more than 4 years, experience in the IT industry, architecting giant products. He has been involved with design workflow for frameworks and applications. He is a strong team player, quick, and versatile. He has developed several financial web applications with cutting edge technologies.

He has mastered every stage of software development. He can interpret business and client requirements and has helped teams to build scalable technical solutions. His expertise extends to both frontend and backend technologies, including Java, Spring, Hibernate, AngularJS, Node.js, and JavaScript.

He is also good at delivering training on the latest technology and server-based solutions.

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This book provides practical guidelines on how to use machine learning algorithms to create predictive models. It is very common to learn about machine learning in tutorials using toy, or small, datasets, which is very practical for learning the basic concepts but not enough when trying to apply what you've learned to real problems.

This book covers the main steps to be followed in order to develop predictive models based on machine learning algorithms. Data collection, data treatment, univariate and multivariate analysis, and the application of the most common machine learning algorithms are some of the steps described in this book. This is a programming book, containing several lines of code, therefore you could replicate all the steps described in it.

This book shows how there are no unique modeling possibilities; different existing options in each modeling step are key to achieving accurate and useful models.

The applications included in this book have been based on the financial sector. This is mainly because I am familiar with the information and the problems, and because there is a huge amount of data to apply several techniques to in a way that is representative of problems that can be found in real life.

The theoretical framework of the book is based on explaining the financial crisis and its causes. Would we be able to predict the next financial crisis? If not, at least you will learn very useful techniques for squeezing your data.

This book is a useful textbook for graduate students and is a reference book for researchers and machine learning and big data practitioners who want to know how to deal with large amounts of data and the main problems that arise in both the development of predictive models and the application of machine learning algorithms. It covers fundamental modern topics in machine learning and describes several key aspects of the application of algorithms. The book is focused on credit risk and the financial crisis, so it could also be interesting for researchers in that field.