1st Edition of ‘Machine Learning, Big Data, and Data Mining: Overview and Insights

The first version of the Wiley and SAS Business Series on Big Data, Data Mining, and Machine Learning offers a thorough introduction to the concepts and practical uses among those three interrelated topics in contemporary business.

For many of those who are fascinated by this topic, there is a tonne of free machine learning books online that may offer a wealth of information. These publications cover a wide range of subjects, from fundamental ideas to sophisticated approaches.

• Author: Jared Dean
• Edition: 1^st Edition
• Publisher: Wiley
• Published: May 16, 2014          
• Language: English
• Pages: 289 Pages
• File Size: 3.72 MB
• ISBN: 978-1118618042

Some popular free machine learning books include “Big Data, Data Mining, and Machine Learning (Wiley and SAS Business Series) 1st Edition ” by Jared Dean, “These good machine learning books are available in digital format and can be accessed for free on the authors’ websites or through online platforms such as Programming Coding.

However, it’s important to note that free machine-learning books may not always be as comprehensive or up-to-date as their paid counterparts. They may also lack the level of support and guidance that comes with a structured course or program. Nonetheless, they can still be an invaluable resource for anyone looking to explore the world of machine learning. This book considers general books.

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The ideas Jared discusses in this book are very beneficial for the students I teach and will give them a deeper understanding of the potential that may be unleashed when a business starts to use its data. The research papers and examples are very helpful in giving students a sense of what is feasible. Jared's enthusiasm for analytics is evident in his work, and he excels at simplifying complex concepts for a wide range of readers.

At SAS, Jared Dean teaches the Business Knowledge Series and serves as a predominant data scientist. He has created cutting-edge analytics for mobile data, finance, and entertainment. The SAS kernel for Jupyter and SASPy is one of the Python and R projects he has created and administers. He is a regular lecturer and researcher outside of SAS and an adjunct professor at NCSU, Duke, and Elon Universities. Jared has several data mining patents under his belt. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is written by Jared (John Wiley & Sons, Inc., May 2014). One of the top five big data books to read this season is my own.

About Machine Learning:

A good machine learning book starts by providing a clear introduction to the concept of big data, discussing the challenges and opportunities it presents for organizations. It then moves on to explain the fundamentals of data mining, including the different types of algorithms used to extract useful information from large datasets. Finally, it delves into the world of machine learning, describing how algorithms can learn from data and improve their performance over time.

One of the key strengths of this good machine learning book is its practical focus. It includes numerous real-world examples of how big data, data mining, and machine learning are being used to drive business value across a range of industries. These examples are supplemented by case studies and hands-on exercises that give readers a chance to apply the concepts they've learned to real-world problems.

The authors also take care to provide a balanced perspective on the challenges and limitations of these technologies. They discuss the ethical implications of big data and machine learning and provide guidance on how to avoid common pitfalls such as bias and overfitting.

Overall, the Wiley and SAS Business Series 1st Edition on Big Data, Data Mining, and Machine Learning is an excellent resource. For anyone looking to gain a deeper understanding of these critical fields. Whether you're a business leader looking to leverage data for competitive advantage. A data scientist seeking to improve your skills, or a student just starting out in the field, this book has something to offer.

Table of Contents:

One Part: The Computing Environment

• Chapter 1: Hardware

• Chapter 2: Distributed Systems

• Chapter 3: Analytical Tools

Second Part: Turning Data into Business Value

• Chapter 4: Predictive Modeling

• Chapter 5: Common Predictive Modeling Techniques

• Chapter 6: Segmentation

• Chapter 7: Incremental Response Modeling

• Chapter 8: Time Series Data Mining

• Chapter 9: Recommendation Systems

• Chapter 10: Text Analytics

Third Part: Success Stories of Putting It All Together

• Chapter 11: Case Study of a Large U.S.‐Based

• Chapter 12: Case Study of a Major Health Care Provider

• Chapter 13: Case Study of a Technology Manufacturer

• Chapter 14: Case Study of Online Brand Management

• Chapter 15: Case Study of Mobile Application

• Chapter 16: Case Study of a High‐Tech Product Chapter 17: Looking to the Future