Machine Learning Introduction
Artificial Intelligence(AI), and Machine Learning(ML) are the words you are listening to nowadays a lot. Is it?
If yes, and you want to know about these topics then you are at the right place.
Today, We shall discover these topics and shall get a brief introduction about them.
1. What is Artificial Intelligence?
Artificial intelligence is the ability of machines to perform certain tasks, which need the intelligence showcased by humans and animals. This definition is often ascribed to Marvin Minsky and John McCarthy from the 1950s, who were also known as the fathers of the field.
It is a broad subject that contains machine learning as a sub-category.
2. What is Machine Learning?
Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its logic based on the data.
2.1. When and How all this start?
It was in the 1940s when the first manually operated computer system, ENIAC, was invented. At that time the word “computer” was being used as a name for a human with intensive numerical computation capabilities, so, ENIAC was called a numerical computing machine! Well, you may say it has nothing to do with learning?! WRONG, from the beginning the idea was to build a machine able to emulate human thinking and learning.
In the 1950s, we see the first computer game program claiming to be able to beat the checker's world champion. which made history.
Thanks to statistics, machine learning became very famous in the 1990s. The intersection of computer science and statistics gave birth to probabilistic approaches in AI. This shifted the field further toward data-driven approaches. As a highlight, IBM’s Deep Blue system beat the world champion of chess, the grandmaster Garry Kasparov.
We can consider the 90s as one of the golden eras of machine learning. During the decade there were significant contributions to the field.
2.2. Machine Learning Algorithms
You can think of machine learning algorithms as falling into one of two main categories — supervised learning and unsupervised learning. The difference is simple but critical.
2.2.1. Supervised learning
Supervised learning is an approach to creating artificial intelligence (AI), where a computer algorithm is trained on input data that has been labeled for a particular output. The model is trained until it can detect the underlying patterns and relationships between the input data and the output labels, enabling it to yield accurate labeling results when presented with never-before-seen data.
Confused??
Let’s make it easier with an example.
Let’s say you are a real estate agent. Your business is growing, so you hire a bunch of new trainee agents to help you out. But there’s a problem — you can glance at a house and have a pretty good idea of what a house is worth, but your trainees don’t have your experience so they don’t know how to price their houses.
To help your trainees (and maybe free yourself up for a vacation), you decide to write a little app that can estimate the value of a house in your area based on its size, neighborhood, etc., and what similar houses have sold for.
So, you write down every time someone sells a house in your city for 3 months. For each house, you write down a bunch of details — number of bedrooms, size in square feet, neighborhood, etc. But most importantly, you write down the final sale price. Using that written data, we want to create a program that can estimate how much any other house in your area is worth. This is called supervised learning.
Sounds great??
2.2.2. Unsupervised Learning
Unsupervised Learning is a machine learning technique in which the users do not need to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with unlabeled data.
So much technical??
Here is an example.
Let’s go back to our original example with the real estate agent. What if you didn’t know the sale price for each house? Even if all you know is the size, location, etc. of each house, it turns out you can still do some cool stuff.
This is kind of like someone giving you a list of numbers on a sheet of paper and saying “I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
So, what could do with this data? For starters, you could have an algorithm that automatically identified different market segments in your data. Maybe you’d find out that home buyers in the neighborhood near the local college really like small houses with lots of bedrooms, but homebuyers in the suburbs prefer 3-bedroom houses with lots of square footage. Knowing about these different kinds of customers could help direct your marketing efforts. Getting these insights from the data is simple Unsupervised learning.
We end our interaction here, This was a brief Introduction to Artificial Intelligence and Machine Learning.
“Education without application is just entertainment.”
— Tim Sanders
