What is Machine Learning?
By: Jesal P.
Tags: Artificial Intelligence, Datasciencecourse, Machine Learning, Supervised Learning, Unsupervised Learning

image courtesy: shutterstock.com

Hello fellow learners and readers,

I have planned to publish a series of episodes on Machine Learning.

As myself being a ML enthusiast and a practitioner of Data Science , I would like to share some of my takeaways from what I’ve gathered.

Readers are always welcome for correcting and discussing about the topics that are presented. Looking forward for a healthy ML environment !

Let’s dig in !

Machine Learning — It’s a new fancy word originated as a subset of Artificial Intelligence. I always think why it’s always referred as Machine Learning and Artificial Intelligence and not Machine Intelligence and Artificial Learning ? — well readers drop down your idea in the comment section !

Let’s dive into some basics.

Once you understand the business requirement and gather the prerequisite datasets for the exploratory data analysis(EDA) (will be featuring this in other parallel episode). Following the CRISP_DM framework.

The next interesting stage is — “Modelling”

Modelling uses machine learning algorithms, in which the machine learns from the data just like humans learn from their “experience”.

Before going into the description of Machine Learning, Let’s look at some real life experiences.

Have you ever found a bunch of e-mails in your spam box ? —

Think how they landed there?

What piece of code might have decided / generated flag that made a particular e-mail to spam / ham ?

Think how they landed there? What piece of code might have decided / generated flag that made a particular e-mail to spam / ham ? Ever tried to get the selling price of your house/property from a broker? —

They usually ask you some questions/or do the survey themselves regarding :

Area of property, number of rooms, number of floors, etc..

And with this data they try to predict a sale value of your house.

Assume you want to get a fair price of your property without the influence of brokerage. So you take help of a back-end programmed web-application for this.

How would that application predict the price?

They usually ask you some questions/or do the survey themselves regarding : Area of property, number of rooms, number of floors, etc.. And with this data they try to predict a sale value of your house. Assume you want to get a fair price of your property without the influence of brokerage. So you take help of a back-end programmed web-application for this. How would that application predict the price? Let’s say you own a food truck — (I would one day ! all time foody)

And you would like to know where to make a stand for your food truck?

You open the maps and try finding out the area.. But is that a good way?

No ! without a survey of what people in different areas like to eat and What is the approximate count of population likely to have your product? It will be a risky factor for your food truck. So you might be thinking how will a machine solve this problem?

Now let me correlate the above examples with machine learning methods.

Fundamentally there are two learning methodologies Machine Learning is bifurcated:

Supervised Learning : Firstly there will be a past records/past datasets here. Basically you will be knowing what to figure out from the output.

This is further divided into two learning methods:

— Regression learning algorithm: It predicts the numerical/continuous output variable — predicting the price of your property .

You feed the machine with the historical data of the property sold with support data like number of room, number of floors, etc.

And the machine tries to figure out some pattern in the data to generate a predicted output.

— Classification Learning algorithm: It predicts the categorical output — What does that mean? — Have you got a spam e-mail ?

So if you think fundamentally there are certain words that make an e-mail spam or ham. Think about words like — [“hurry”, “last chance”, “sensual”, “lottery”, “adult”, “ATM PIN”]… what would be your thoughts ? It’s a TRAP.

So here you know that if any of such words appear in the e-mail you know where to send it to. Similarly you can teach a machine provided a list of such spam words along with your own list. You can teach your e-mail application by sending some unwanted e-mails to spam box manually. Next time if such related content e-mail comes you’ll find it in spam.. So now your machine knows what to do !

Unsupervised Learning :You will find a related records for this. But you don’t know what kind of the spread is / how the data is divided into? What to name the data ?

— Clustering Learning algorithm: It has got no predefined labels / notion allocated.

It helps forming labels and group/clusters of such labels. — Remember your food truck?

In this case you don’t know which area to target? So you get the data from survey of people preferring certain categories of food from different locations. Now with the nearest relevance to your food item you plot/circle some areas where your food like item is most liked by the area.

After locating the areas you find the approximate number of people liking the product in the areas. Of-course ! you won’t be going to a place where 20% of people show interest in your product — Primarily for your business start.

So you set a cutoff of 70–80% . So now from the scatter you round off the area where you have more than 70–80% people likely to have your product. And you also cluster different categories from your food truck like burgers, pastas,pizzas ,dosas,etc.. Imagine doing all these research and statistic by yourself !

Now think of all this to be done by a machine ? — here machine will analyze the data and figure out how many people like burgers in which area from the map. And this will create a cluster forming labels —burgers, pizzas, pastas, dosas , etc. And from the rules/constraints you decide the machine will tell you which area to target !

Well this was the basic explanation of machine learning terminologies that I understood. In the next episode I’ll be taking you to an exploration on the components for building a machine learning model and its process flow !