Tag: Machine Learning

Making Algorithms Useful

This is an update of a post that originally appeared on December 2, 2015.

Writing about machine learning and deep learning in my various books has been interesting because it turns math into something more than a way to calculate. Machine learning is about having inputs and a desired result, and then asking the machine to create an algorithm that will produce the desired result from the inputs. It’s about generalization. You know the specific inputs and the specific results, but you want an algorithm that will provide similar results given similar inputs for any set of random inputs. This is more than just math. In fact, there are five schools of thought (tribes) regarding machine learning algorithms that Luca and I introduce you to in books such as Machine Learning Security Principles, Algorithms for Dummies, 2nd Edition, Python for Data Science for Dummies, and Machine Learning for Dummies, 2nd Edition:

  • Symbolists: The origin of this tribe is in logic and philosophy. This group relies on inverse deduction to solve problems.
  • Connectionists: The origin of this tribe is in neuroscience. This group relies on backpropagation to solve problems.
  • Evolutionaries: The origin of this tribe is in evolutionary biology. This group relies on genetic programming to solve problems.
  • Bayesians: This origin of this tribe is in statistics. This group relies on probabilistic inference to solve problems.
  • Analogizers: The origin of this tribe is in psychology. This group relies on kernel machines to solve problems.

Of course, the problem with any technology is making it useful. I’m not talking about useful in a theoretical sense, but useful in a way that affects everyone. In other words, you must create a need for the technology so that people will continue to fund it. Machine learning and deep learning are already part of many of the things you do online. For example, when you go to Amazon and buy a product, then Amazon makes suggestions on products that you might want to add to your cart, you’re seeing the result of machine learning. Part of the content for the chapters of our book is devoted to pointing out these real world uses for machine learning.

As I’ve written new books and updated existing ones, I’ve seen an almost magical progression in the capabilities of machine learning and deep learning applications such as ChatGPT, Chat Generative Pre-Trained Transformer, which can produce some pretty amazing output.

Some of these applications, such as Siri and Alexa, continue to learn as you use them. The more you interact with them, the better they know you and the better they respond to your needs. The algorithms that these machine learning systems create get better and better as the database of your specific input grows. The algorithms are tuned to you specifically, so the experience one person has is different from an experience another person will have, even if the two people ask the same question.

Machine learning is a big mystery to many people today, while other people have gained enough experience to have strong opinions about it. Because I continue to write new machine learning/deep learning books and update others, it would be interesting to hear your questions about machine learning and deep learning. After all, I’d like to tune the content of my books to meet the most needs that I can. Where do you see this technology headed? What confuses you about it? Talk to me at [email protected].

Introducing Machine Learning Security Principles

Are you a manager, researcher, or novice data scientist who works with data regularly, yet can’t really understand the technobabble found in security books that are supposed to help you secure the data you work with? Machine Learning Security Principles is all about providing you with full disclosure of all of the security threats that can affect your data in a detailed way that is also understandable. The idea is to understand the threats and understand the players in the security arena so you can create a strategy that will ensure your data remains safe without feeling completely lost in the language used by most books today.

Machine Learning Security Principles looks at data from every possible perspective, which means that you’ll learn more than just collection and storage methods. It isn’t just the hackers and disgruntled employees that are the problem. You now have to deal with governments that tell you how to collect data properly and face the wrath of the pubic at large when the data is collected in a less than ethical manner, even when no laws have been broken. In addition, it’s more than just the data, it’s also the system that holds the data, the application the uses the data, and the users who enter the data that can become problematic. With this in mind, here are some things that you’ll learn when reading this book:

  • Learn methods to prevent illegal access to your system.
  • Discover detection methods when access does occur.
  • Employ machine learning techniques to determine motivations.
  • Mitigate hacker access using a variety of methods.
  • Repair damage to your data and applications.
  • Use ethical data collection methods to reduce security risks.

A major complaint with most books on the market is that there is an expectation that you’re not only an expert coder, but that all you want is to see code. That’s fine if you’re already a seasoned security expert, but then seasoned security experts really don’t need books like this one. Machine Learning Security Principles provides you with several ways to learn about security issues:

  • References to actual security break-ins and the results of them.
  • Block diagrams showing how various kinds of security issues occur.
  • Explanatory text that helps you understand what precisely can happen and how to prevent.
  • Example code that you can use to discover how various security techniques work.
  • Example data and the techniques you can use to work with it.
  • Resources that you can use to augment your security plan.
  • Online tools you can use to more fully explore security issues.

In short, Machine Learning Security Principles provides you with several methods of learning about security in an easy to use manner. It doesn’t take a one size fits all approach. Please let me know if you have any questions about my new book by contacting me at [email protected].

Finding and Employing Data Science Tools

Python for Data Science for Dummies introduces you to a number of common libraries used for data science experimentation and discovery. Most of these libraries also figure prominently as part of a data scientist’s toolbox because they provide common functionality needed for every application. It is a great idea for those who are interested in expanding their knowledge in data science and how it can be applied to the field of Artificial Intelligence (AI). You can learn more about some of the basic principles such as applying, developing, leveraging and creating data science projects. However, these libraries are only the tip of the data science toolbox. Because data science is such a new technology, you can find all sorts of tools to perform a wide range of tasks, but there is little standardization and some of these tools are hard to categorize so that you know where they fit within your toolbox. That’s why I was excited to see, The data science ecosystem, the first of a three part series of articles that describe some of the tools available for use in data science projects. If you are interested in finding out more about data science, you might want to check out this data science bootcamp for more information. You can also find the other two parts of the article at:

The problem for people who want to explore data science and machine learning today might not be the lack of tools, but the lack of creativity in using them. In order to explore data science, it’s important to understand that the tools only work when your prepare the data properly, employ the correct algorithm, and define reasonable goals. So for those that are looking for suitable tools and aid when looking to start experimenting with data science or machine learning processes they might look to collaborate with other data scientists using this open-source dvc data science platform or one similar that can integrate many other data science tools. No matter how hard you try, data science and machine learning can’t provide you with the correct numeric sequences for the next five lottery wins. However, data science can help you locate potential sources of fraud in an organization. The article, Machine learning and the strategic snake oil reserve, sums up what may be the biggest problem with data science today-people expect miracles without putting in the required work. Fortunately, there are new tools on the horizon to make languages, such as Python, and products, such as Hadoop, easier for even the less creative mind to use (see Python and Hadoop project puts data scientists first).

Even with a great imagination, the tools available today may not do the job you want as well as they should because the underlying hardware isn’t capable of performing the required tasks. The process is further hampered by a misuse of the skills that data scientists provide (see You’re hiring the wrong data scientists for details). As a result, you need a large number of specialized tools in order to perform tasks that shouldn’t require them. However, that’s the reason why you need to know about the availability of these tools so that you can produce useful results on today’s hardware with a minimum of fuss. Asking the question, “How would Alan Turing fix A.I.?” helps you understand the complexities of the data science and machine learning environments.

Data science, machine learning, data scientists with even greater skills, and better hardware will keep the momentum going well into the future. As the Internet of Things (IoT) continues to move forward and the problem of what to do with all that data becomes even larger, data science will take on a larger role in everyone’s daily life. Count on reading more articles like, Google a step closer to developing machines with human-like intelligence, that describe the proliferation of new hardware and new tools to make the full potential of data science and machine learning a reality. In the meantime, getting the tools you need and exploring the ways in which you can creatively use data science to solve problems is the best way to go for now. Let me know your thoughts on the future of data science at [email protected].