those movie AIs, a machine learning algorithm does precisely what you expect it to do, nothing more. Of course, some of the results are amazing, but keeping expectations in line with what the actual technology can do is important. Otherwise, you’ll promise something that the technology can never deliver, and those adherents whom you were expecting will go looking for the next big thing.
In fact, the uses for machine learning today are quite narrow. As described in the article at https://www.linkedin.com/pulse/machine-learning-its-hard-problems-valuable-toby-coppel, narrow AI, such as the business use of AI to gain insights into huge datasets, relies on well-understood techniques that companies have started to employ within the past decade. The machine can’t infer anything, which limits the use of the machine to the task for which the developer or data scientist designed it. In fact, a good analogy for today’s algorithms is that they’re like a tailored shirt (see the article at https://www.computerworld.com/article/3006525/cloud-computing/why-microsofts-data-chief-thinks-machine-learning-tools-are-like-tailored-shirts.html for more details). You need specialized skills to create an algorithm that is tailored to meet specific needs today, but the future could see algorithms that can tackle nearly any task. Companies that rely on narrow AI need to exercise care in how they develop products or services. A change in product or service offerings might place the data used for the machine learning environment outside the learner algorithm’s domain, reducing the output of the machine learning algorithm to gibberish (or at least making it unreliable).
Using machine learning in an organization also requires that you hire people with the right set of skills and create a team. Machine learning in the corporate environment, where results mean an improvement in the bottom line, is relatively new. Companies face challenges in getting the right team together, developing a reasonable set of goals, and then actually accomplishing those goals. To attract a world-class team, your company has to offer a problem that’s exciting enough to entice the people needed from other organizations. It isn’t an easy task, and you need to think about it as part of defining the goals for creating a machine learning environment.
Part 2
Preparing Your Learning Tools
IN THIS PART …
Creating a Python setup
Performing basic Python tasks
Using Google Colab
Chapter 4
Installing a Python Distribution
IN THIS CHAPTER
Before you can do too much with Python or use it to solve machine learning problems, you need a workable installation. In addition, you need access to the datasets and code used for this book. This chapter tells you how to perform the required Python setups and downloads. Downloading the sample code (found at this book’s page at www.dummies.com) and installing it on your system is the best way to get a good learning experience from the book. This chapter helps you get your system set up so that you can easily follow the examples in the remainder of the book.
ML4D2E; 04; Sample.ipynb and ML4D2E; 04; Dataset Load.ipynb files. (The Introduction tells you where to download the source code for this book.)
ML4D2E (for Machine Learning For Dummies, 2nd Edition) folder of the downloadable source, the R code appears in the ML4D2ER (for Machine Learning For Dummies, 2nd Edition, R code) folder. The R examples don't always precisely follow the Python examples because of the differences in the two languages, but the R examples are heavily annotated so that you can follow along. Using either language will allow you to reach the desired result.
Using Anaconda for Machine Learning
You can use a number of packages to perform machine learning tasks. In fact, too many exist to discuss adequately in a single chapter. To make it easier for you to focus on machine learning rather than a software package, the first section that follows tells you how to obtain your copy of Anaconda, the Anaconda3-2020.07 version. This book uses Anaconda for a number of reasons, as explained in the next section. However, if you’re using a platform where the installation process doesn’t work well (or possibly at all), you can also follow along with the book’s code using Google Colab, as described in Chapter 6. The sections that follow provide you with a brief overview of Anaconda as a product.
Getting Anaconda
The basic Anaconda package is a free download that you obtain at https://www.anaconda.com/products/individual. Simply click Download to see the list of available downloads; then click the individual link for your platform to obtain access to the free product. Anaconda supports the following platforms:
Windows 32-bit and 64-bit (the installer may offer you only the 64-bit or 32-bit version, depending on which version of Windows it detects)
Linux 64-bit (x86 and PowerPC 8/9 installers)
Mac OS X 64-bit (graphical and command-line installer)
In all cases, you want the Anaconda3-2020.07 version of the product. If you can’t find the correct version on the main Anaconda page, you can obtain it at https://repo.anaconda.com/archive/.
