Simple Machine Learning for Programmers: Beginner’s Intro to Using Machine Learning, Deep Learning, and Artificial Intelligence for Practical Applications

Simple Machine Learning for Programmers: Beginner’s Intro to Using Machine Learning, Deep Learning, and Artificial Intelligence for Practical Applications

English | 2018 | 58 Pages | EPUB, AZW3 | 10 MB

Machine Learning for Beginners
This book is an experiment for me.
After years of teaching successful deep learning and machine learning courses online, I’ve come to notice a few patterns.
One of them is that a large subset of students just RUN AWAY at the sight of math.
This is somewhat problematic since machine learning is essentially applied math (e.g. instead of just writing down equations you’re programming the equations into a computer in order to perform some useful task).
A lot of students have asked me, “Lazy Programmer, is it possible to learn machine learning with an API?”
Indeed, in programming, the idea that you can have abstractions for different components in a large system is a very powerful concept.
It means that instead of having to understand every single line of code in great detail, you instead trust that other programmers, all building their respective components, have completed their respective piece of the puzzle.
An API is a contract. It says, “You give me this, and I promise to give you this back.”
It allows you to focus on writing your part of the system only, since you trust that everyone else has performed their jobs correctly.
This book is an experiment to see how well students can learn some of the basic concepts in machine learning, without having to do any math or theoretical work.
If you are a programmer that wants to learn machine learning by taking the “API approach”, then this book is perfect for you.
I have courses totaling over 100 hours of math, algorithms, and theory. If that’s what you’re looking for, this is not it.
In this book, we’re going to learn how to solve practical problems using machine learning. We’re even going to see how to apply a simple deep neural network without having to understand how they work mathematically.
We’re going to make heavy use of the Numpy Stack:

  • Numpy
  • Scipy
  • Matplotlib
  • Pandas
  • Scikit-Learn

Another goal of this book is to prepare you to learn more advanced topics. One such topic is Natural Language Processing (NLP). Essentially, NLP is the application of machine learning to text.
A common pattern I saw in students was that they were often trying to tackle NLP, without knowing about machine learning in the first place!
This leads to all sorts of weird misconceptions and incorrect assumptions. You can’t apply something if you don’t what that something is.
Of course, undue frustration is the result.
Until now, I had no place to point these students towards, in order to learn the basics of machine learning.
The problem with most other resources out there is that they try to make machine learning sound as complex as possible. They aim to make machine learning sound almost magical.
They too make use of APIs, but they treat the code like a magic spell rather than trying to truly understand what machine learning does.
I prefer the realistic approach.
Instead of making machine learning sound magical, I want to make machine learning sound as dumb as possible. In fact, I’m going to demonstrate to you that it’s nothing more than a geometry problem.
This is real intuition.
Instead of doing magic, you can use basic spatial reasoning skills to understand machine learning, and this puts you in a much better place to actually implement machine learning algorithms – the next step in becoming a machine learning master.