Let us begin. I am pretty excited about this post, here onward we start to learn how to do Machine Learning.
From whatever posts I have followed, somehow, people take an approach of first explaining the types of things one can do using Machine Learning, namely classification, clustering, etc.
Let us take a different path. Let us head straight to solve a problem and get our hands dirty. We will also, of course, come to a point where we will talk about types of problems that Machine Learning solves, but let us first get introduced to the problem solving.
We will be using Python, examples are tested with Python 3.5.
We need numpy, scipy and matplotlib packages. How to install these is not covered here, as that will drift us. You can install Anaconda, or follow along OS specific post for installation.
In the last post, we saw few applications of Machine Learning. continuing on the same grounds, more applications are presented in this post.
Just as a reminder, the point is to get us motivated enough so that we actually see how to do it ourselves.
(3)Marketing: In the age of globalization, bringing buyers and sellers together for mutually adventurous exchange is not an easy job. Simply because of the diversity of the influence factors, such as economic trends, social forces, political conditions, etc. Machine Learning is already so deeply involved that people cannot even imagine not using it for the Marketing.
As promised last week, the question - Why should I care? will be addressed in this and the next, connecting post.
In general, what do you think, when do people care about something?
If something is having a direct or indirect impact on their lives, they usually care. Machine Learning has advanced the ability of human being to perform almost every GOFAI task in a smarter way.
There are many aspects like increased computation speed, data availability, affordable data storage, which can explain the rise of the Machine Learning era. But the most prominent one is the Practical Applications of Machine Learning. Simply put, it actually works in real life! It is being used by tens of thousands of companies, just to craft smarter machines.
Hence, rather than extrapolating reasons of why people are using Machine Learning, more fun is identifying where and why they are using it.
Let’s get started. While working with people from different backgrounds, I came to an understanding that, overall, there are many misconceptions about Machine Learning.
One set of people think of Machine Learning as an area full of Mathematical jargon. On top of this thinking they either come close or keep a safe distance.
Another group thinks this is highly advanced stuff, and they will not be able to pick it up. Therefore, they are too afraid to approach a problem
Few people think of it as a magic box, which will chew in any data and produce some unrealistic results.
From my experience, I also think that people who say ‘I know how to do it’ actually miss few fundamental things.
I think there is a need to think of this domain without having any biases, as well as understand and accept the fundamental rules.
This guide will help you installing OpenGL. I am only targetting fixed-function pipeline, i.e., we won’t test programmable pipeline’s functionality after installation.
Programmable pipeline is subject to your hardware and device drivers which in turn determine which OpenGL version is supported. If you do not know what I am talking about - look here
Let us get started.
For Linux Users
If you are on debian (ubuntu) system sudo apt-get install freeglut3-dev