Machine Learning in Python: Hands on Machine Learning with Python Tools, Concepts and Techniques

Machine Learning In Python

Hands on Machine Learning with Python Tools, Concepts and Techniques

Disclaimer

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Table of Contents

Disclaimer

What is Machine Learning?

Why use Python?

Regression Analysis using Python

Implementing an Artificial Neural Network

A 90 Day Plan for Machine Learning with Python

Conclusion

What is Machine Learning?

How Programming Normally Works

The usual method of programming is quite linear, even in places where it seems nonlinear. The most common insult that some programmers use to refer to machine learning is that it is just a bunch of if... else statements where the machine is not actually learning. It is very easy to understand how these programmers come to understand this, but it is important to realize that they are only half right.

Let’s look at how something like a website and Photoshop works, considering how widely the manner in which they operate is different. A website is a collection of HTML, CSS, and Javascript with whatever backend code implementation they plan to use. The website itself does not normally install anything on the user desktop and utilizes features that are already there.

The only mechanism that provides change is the web browser itself and it is only when the web browser supports changes in those languages do those languages really have access to new features. In order to construct the front-end of the website, one has to load the HTML, which will then load the CSS in the Head or the Body areas of the page and load the Javascript in, usually, the Body area of the page near the footer. Therefore, it is linearly loaded no matter how interconnected the web pages may seem.

In Photoshop, the implementation is definitely different due to the fact that it is a program that must be installed on a computer. To the average individual, Photoshop looks like a self-contained unit that can be utilized on every platform. However, Photoshop must utilize and have access to graphical standards only found in drivers for Graphics Cards. In order to draw a line, Photoshop normally has to make a call to the Direct X 11 or Direct X 12 or Vulkan or OpenGL libraries. No one really knows which library it calls to or if it calls to all of them, but all graphics-based programs have to call on existing libraries. This doesn’t become apparent until the program encounters an error.

You might ask how I know this and it really has to deal with the variation of Graphics Cards on the market. You have Intel, AMD, and NVidia all making their own versions of Graphics Chips, with each version of these chips running on the previously mentioned libraries and even older ones. With AMD alone, I know that the past 10 years have seen Direct X 9, Direct X 10, Direct X 11, and Vulkan chip libraries. These libraries provide a consistent basis for function calls across the variety of Graphics Chips in the market. It would be impractical for Adobe, developers of Photoshop, to create their software from complete scratch for each Graphics Chip in existence when there are pre-existing libraries that other companies maintain that cut the workload significantly.

Therefore, in order for a program like Photoshop to even work, it has to have a linear access to already implemented resources. Photoshop, itself, is very modular but still linear. You can see this in how it structures its’ menus. I click on Filter to find the Blur category where I can use the Gaussian Blur equation. Photoshop can be seen more like a library of different image related equations that have sub-equations to ultimately create a linear stack of Layers as they are referred to in Photoshop. Therefore, while the tools are modular, they are nested linearly and applied to the image in a chronologically linear methodology.

Having this in mind and having seen programs and websites work like this for decades, it is understandable that Machine Learning could be seen as nothing more than if... else statements. The problem doesn’t rely on how programming works, but rather on how if... else statements are seen. For instance, if true then this else then that is a valid way to teach new programmers how to understand if... else statements. The programmers who compare Machine Learning to this could say if (feature has curve) then feature is a, b, c... else feature is L, A, E... and this could very well be a valid representation of how a network might work. However, that is how the human mind works and we learn all the time so what’s the problem?

How We Define Learning

The problem, therefore, is the definition of what it means to learn, and this is indeed a philosophical discussion. You might have been asking why I have laid this out in such a manner, but it is truly important to understand that machine learning works differently than the average programming as it has been practiced. It is different not because of how it is programmed, but with what intent it is programmed for. This is why the philosophy is also important as it determines how one goes about making and implementing machine learning.

How does the human mind learn? It learns through practicing until it gets it mostly right. Therefore, our recognition usually fails us the first few times that we attempt to apply it. It is only through repeated failure that human minds find their