Beta Readers Needed for Machine Learning for Dummies

Do machines really learn, or do they simply give the appearance of learning? What does it actually mean to learn and why would a machine want to do it? Some people are saying that computers will eventually learn in the same manner that children do. However, before we get to that point, it’s important to answer these basic questions and consider the implications of creating machines that can learn.

Like many seemingly new technologies, machine learning actually has its basis in existing technologies. I initially studied about artificial intelligence in 1986 and it had been around for a long time before that. Many of the statistical equations that machine learning relies upon have been around literally for centuries. It’s the application of the technology that differs. Machine learning has the potential to change the way in which the world works. A computer can experience its environment and learn how to avoid making mistakes without any human intervention. By using machine learning techniques, computers can also discover new things and even add new functionality. The computer is at the center of it all, but the computer output affects the actions of machines, such as robots. In reality, the computer learns, but the machine as a whole benefits.

Machine Learning for Dummies assumes that you have at least some math skills and a few programming skills as well. However, you do get all the basics you need to understand and use machine learning as a new way to make computers (and the machines they control) do more. While working through Machine Learning for Dummies you discover these topics:

  • Part I: Introducing How Machines Learn
    • Chapter 1: Getting the Real Story about AI
    • Chapter 2: Learning in the Age of Big Data
    • Chapter 3: Having a Glance at the Future
  • Part II: Preparing Your Learning Tools

    • Chapter 4: Installing a R Distribution
    • Chapter 5: Coding in R Using RStudio
    • Chapter 6: Installing a Python Distribution
    • Chapter 7: Coding in Python Using Anaconda
    • Chapter 8: Exploring Other Machine Learning Tools
  • Part III: Getting Started with the Math Basics

    • Chapter 9: Demystifying the Math behind Machine Learning
    • Chapter 10: Descending the Right Curve
    • Chapter 11: Validating Machine Learning
    • Chapter 12: Starting with Simple Learners
  • Part IV: Learning from Smart and Big Data
    • Chapter 13: Preprocessing Data
    • Chapter 14: Leveraging Similarity
    • Chapter 15: Starting Easy with Linear Models
    • Chapter 16: Hitting Complexity with Neural Networks
    • Chapter 17: Going a Step Beyond using Support Vector Machines
    • Chapter 18: Resorting to Ensembles of Learners
  • Part V: Applying Learning to Real Problems
    • Chapter 19: Classifying Images
    • Chapter 20: Scoring Opinions and Sentiments
    • Chapter 21: Recommending Products and Movies
  • Part VI: The Part of Tens
    • Chapter 22: Ten Machine Learning Packages to Master
    • Chapter 23: Ten Ways to Improve Your Machine Learning Models
    • Online: Ten Ways to Use Machine Learning in Your Organization

As you can see, this book is going to give you a good start in working with machine learning. Because of the subject matter, I really want to avoid making any errors in book, which is where you come into play. I’m looking for beta readers who use math, statistics, or computer science as part of their profession and think they might be able to benefit from the techniques that data science and/or machine learning provide. As a beta reader, you get to see the material as Luca and I write it. Your comments will help us improve the text and make it easier to use.

In consideration of your time and effort, your name will appear in the Acknowledgements (unless you specifically request that we not provide it). You also get to read the book free of charge. Being a beta reader is both fun and educational. If you have any interest in reviewing this book, please contact me at John@JohnMuellerBooks.com and will fill in all the details for you.

 

Author: John

John Mueller is a freelance author and technical editor. He has writing in his blood, having produced 99 books and over 600 articles to date. The topics range from networking to artificial intelligence and from database management to heads-down programming. Some of his current books include a Web security book, discussions of how to manage big data using data science, a Windows command -line reference, and a book that shows how to build your own custom PC. His technical editing skills have helped over more than 67 authors refine the content of their manuscripts. John has provided technical editing services to both Data Based Advisor and Coast Compute magazines. He has also contributed articles to magazines such as Software Quality Connection, DevSource, InformIT, SQL Server Professional, Visual C++ Developer, Hard Core Visual Basic, asp.netPRO, Software Test and Performance, and Visual Basic Developer. Be sure to read John’s blog at http://blog.johnmuellerbooks.com/.

When John isn’t working at the computer, you can find him outside in the garden, cutting wood, or generally enjoying nature. John also likes making wine and knitting. When not occupied with anything else, he makes glycerin soap and candles, which comes in handy for gift baskets. You can reach John on the Internet at John@JohnMuellerBooks.com. John is also setting up a website at http://www.johnmuellerbooks.com/. Feel free to take a look and make suggestions on how he can improve it.