Statistical and Machine-Learning Data Mining, learn about big data mining, 3rd Edition PDF

Statistical and Machine-Learning Data Mining, 3rd Edition, authored by Bruce Ratner, is a comprehensive guide to the world of data mining. The book is designed to help readers understand the principles of data mining and how it can be applied to real-world problems. It covers a wide range of topics, including data exploration, modeling, clustering, classification, and association rules.

Programming Coding a free PDF of the Statistical and Machine-Learning Data Mining, 3rd Edition book is available. However, the book itself provides a comprehensive overview of data mining, covering both statistical and machine-learning techniques. It includes discussions on data exploration, modeling, clustering, classification, C++, and association rules.

• Author: Bruce Ratner
• Edition: 3rd Edition
• Publisher: Chapman and Hall/CRC
• Published: June 1, 2017  
• Language: English
• Pages: 691 Pages
• File Size: 7.5 MB
• ISBN: 978-1498797603

In this article, Programming Coding discusses the book and its availability as a free PDF download. One of the great features of Statistical and Machine-Learning Data Mining, Third Edition is that it is available as a free PDF download. The author, Bruce Ratner, has made the book available on his website, bruceratner.com. The PDF is available for download without any registration or subscription fees. This makes it accessible to anyone who wants to learn about data mining, regardless of their financial resources, and also considers general books.

Available On:

Amazon

Programming Coding gives you the best book on machine learning, in which you can learn about Statistical and Machine-Learning Data Mining as a free PDF available. To download the free pdf by clicking the button given below;

The book also covers the use of decision trees, neural networks, and support vector machines, among other techniques, and provides guidance on how to handle missing values and outliers. Overall, the book is an excellent resource for anyone interested in data mining and predictive modeling.

The book begins with an introduction to data mining and its applications, followed by a discussion of statistical and machine-learning techniques. The author explains the differences between supervised and unsupervised learning and introduces the concept of overfitting. The book also covers the use of decision trees, neural networks, and support vector machines, among other techniques.

About Author:

Bruce Ratner is an American statistician and data mining expert with over 30 years of experience in the field. He is the founder and president of DM STAT-1 Consulting, a company that provides statistical consulting services to businesses and organizations in various industries. Ratner holds a Ph.D. in statistics from Florida State University and has worked with clients in a wide range of industries, including healthcare, finance, and marketing.

In addition to his consulting work, Ratner is also an author and has published several books on data mining and statistical analysis, including "Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data," which is now in its third edition. His research and work have been widely cited in the field of statistics and data mining.

Ratner is known for his expertise in using statistical and machine-learning techniques to help businesses and organizations make better decisions based on their data. He is also a regular contributor to academic journals and conferences and has been recognized for his contributions to the field of statistics. Overall, Ratner is a well-respected figure in the field of data mining and has made significant contributions to the advancement of statistical analysis and predictive modeling.

About Book:

Data mining involves the use of statistical and machine-learning techniques to extract valuable insights from data. Statistical and Machine-Learning Data Mining, Third Edition is a comprehensive guide that provides an in-depth understanding of the different techniques and tools used in data mining.

One of the strengths of this book is the detailed discussion of data preprocessing. The author covers a range of techniques, including data normalization, attribute selection, and dimensionality reduction. The book also provides guidance on how to handle missing values and deal with outliers.

Another strength of this book is the emphasis on practical applications. The author provides a range of case studies and examples from real-world problems, including marketing, finance, and healthcare. The book also includes a discussion of data mining tools, such as Weka and RapidMiner.

The third edition of Statistical and Machine-Learning Data Mining has been updated to reflect recent advances in the field. The author has added a new chapter on deep learning, which is becoming increasingly important in areas such as image and speech recognition. The book also includes a discussion of ensemble learning, which involves combining the outputs of multiple models to improve accuracy.

Overall, Statistical and Machine-Learning Data Mining, 3rd Edition, is an excellent resource for anyone interested in data mining. It provides a comprehensive overview of the field, covering both statistical and machine-learning techniques. The book is well-written and easy to follow, with plenty of examples and case studies to help readers understand the concepts. It is an essential reference for anyone working in data science or related fields.

Table of Contents:

• Chapter No 1: Introduction
• Chapter No 2: Science Dealing with Data: Statistics and Data Science
• Chapter No 3: Two Basic Data Mining Methods for Variable Assessment
• Chapter No 4: CHAID-Based Data Mining for Paired-Variable Assessment
• Chapter No. 5: The Importance of Straight Data Simplicity and Desirability for Good Model-Building Practice
• Chapter No 6: Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data
• Chapter No 7: Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment
• Chapter No 8: Market Share Estimation: Data Mining for an Exceptional Case
• Chapter No 9: The Correlation Coefficient: Its Values Range between Plus and Minus 1, or Do They?
• Chapter No 10: Logistic Regression: The Workhorse of Response Modeling
• Chapter No 11: Predicting Share of Wallet without Survey Data
• Chapter No 12: Ordinary Regression: The Workhorse of Profit Modeling
• Chapter No 13: Variable Selection Methods in Regression: Ignorable Problem, Notable Solution
• Chapter No 14: CHAID for Interpreting a Logistic Regression Model
• Chapter No. 15: The Importance of the Regression Coefficient
• Chapter No 16: The Average Correlation: A Statistical Data Mining Measure for the Assessment of Competing Predictive Models and the Importance of the Predictor Variables