Machine Learning For Newbies!

Have you heard a lot about machine learning but only have a fuzzy idea of what that means? Then don’t worry you are not alone. there are a lot of people who tried reading about machine learning got frustrated and gave up wishing someone would just give them a simple and a quick explanation. That’s what this is.

What is Machine Learning?

We define machine learning as a field of study that gives computers the ability to learn without being explicitly learned. you can think of machine learning that there is an algorithm that can tell you something interesting about a set of data without you having to write any code specific to the problem. Instead of writing code, you feed data to the algorithm and it builds its own logic based on the data.

For example, let's say there is a classification algorithm. which can put data into different categories. you can use that same algorithm to classify recognize handwritten numbers could also be used to classify emails into spam or not-spam without changing any line of code. It is the same algorithm but you fed it with different data so it comes up different logic.

Similarly, you can use the same classification algorithm for a lot of different problems. such algorithms are everywhere. you’ve probably used a learning algorithm dozens of times a day without knowing it.

Every time you use a web search engine like google or bing to search something on the internet. one of the reasons it works so well is because of a learning algorithm implemented by Google or Microsoft learned how to rank web pages.

Every time you use facebook or google photos. It recognizes you and your’s friends photos. Is also because of machine learning.

Machine Learning is an umbrella term covering lots of these kinds of generic algorithms.

Two types of Machine Learning Algorithm

Most machine learning algorithm fell into one of two main categories. supervised learning and unsupervised learning.

Supervised Learning

In Supervised learning, we are given a data-set and we already know what a correct output looks like. the task of a supervised learning algorithm is to learn a function that map input to an output based on example input-output pairs. Supervised learning is classified into two types of algorithms “regression” and “classification” algorithm. In a Regression problem, we are trying to predict result within a continuous output. let say you are given a picture of a person and you have to predict the age of that person based upon the picture Such type of problems is called regression problems where we are trying to map the input variable into some continuous function. In a Classification problem, we are trying to predict the result in discrete categories. suppose you have a picture of an animal and you have to classify if it is a dog or not.

Unsupervised learning

Unsupervised learning is completely different from supervised learning. With unsupervised learning, we can solve a problem where we don’t have any idea about the output that we want. we just throw out dataset into an algorithm and then we let the algorithm find the relationship among the variables in the data. For instance, suppose you have a picture which contains dogs and cats in it that algorithm has not seen ever. Thus the algorithm has no idea about the features of dogs and cat so we can’t categorize it in dogs and cats. But it can categorize them according to their similarities, patterns, and differences. in other words, we can easily categorize the picture into two parts. The first part may contain all pics having dogs in it and the second part may contain all pics having cats in it.

Unsupervised learning classified into two categories of algorithms:

• Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
• Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

I Hope this article help you learn one or two things about machine learning. if you want to learn more about machine learning. I highly recommend you read this guide from Adam Geitgey. If you are looking for a course to dig deeper into machine learning then try Andrew ng machine learning course. It is the highest rated machine learning course on the internet.

Thanks for reading! happy learning : )

References:

https://www.geeksforgeeks.org/supervised-unsupervised-learning/

https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471