Machine learning : A simple overview
Tags: machinelearning, python, beginners, productivity
Published: 2023-01-22 16:29:25

> ## A simple explanation of machine learning.

_**Overview of Machine learning:**_


![Machine Learning](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/j8f10pxangnp260mywar.jpeg)

As stated in one of the articles on IBM’s website, “Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.” That’s what they want you to know, but in reality it is a scheme, a plotting of robots to dominate over the humankind.

Nah, I’m just messing around, seriously I’m not that paranoid trust me :-)

_**Types of Machine Learning:**_

So, basically how I understand this big and scary concept is that machine learning is just the same as tutoring or giving lessons to a kid. When a kid wants to learn something or in most cases you want them to learn, we show them some way of identifying, suppose how a car is different then lets say a truck. Or maybe you want the kid to learn about how 4 is smaller than 5. My point being we give them some examples first so that they can learn and identify the same object later on, this is supervised learning.

And over time when that kid becomes an adult they kind of start picking up on patterns and start understanding concepts clearer without always having the need for an example or say a picture of a car to identify a car and this is unsupervised learning. And similarly there are various ways we can train our model, be it supervised, unsupervised, semi-supervised or even reinforcement learning but the meaning of machine learning is to train the model or machine to understand concepts the way human brain does.



![Types of Machine learning](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/0r3couolq2xd2n2ei4xk.png)


In supervised learning, the model is given input known as features and is also given target variables such as a picture of car to a kid to identify whether the next object is a car or not. The model is then tested using the testing data then, its accuracy is measured. Accuracy at first in most cases isn’t that great, there are various ways to increase accuracy such as hyper-parameter tuning, using GridSearchCV etc., but for now we don’t need to get in to that.

Lets have a look at unsupervised learning, in this type of machine learning imagine and try looking at it the way an adult would learn a concept without having any or none prior knowledge about that particular concept. You as an adult would probably try to find some correlations between the data that you are provided and try to come up with a basic understanding of what it would be. Same goes with unsupervised learning, in this only features or input data is provided no output on the given data is shown to the model. The model when trained learns on its own so to speak and tries to find the best fit answer to the testing data. The model sure is less accurate at first but when is a model 100% perfect on first training, hey if it is be cautious of a monster called overfitting. For now, lets not worry too much about why is it a monster just know that it is.

So, that was all on machine learning and some of its types. As this was just an overview I want to keep it short. I’ll try to write more on this topic as I keep learning more and more. Until then happy reading!

> So this is just how I understood the big and scary looking topic like machine learning at first glance. And, I hope you understood somethings too. Also, I hope I didn’t confuse you even further :-\

Thank you for reading!!