Getting Started with Machine Learning!
Published: 2023-01-02 17:16:29

&nbsp;&nbsp;&nbsp;&nbsp; The word machine learning might instilled wonders in your mind like mine and for the past few months I was trying to unravel the mystery behind the black curtains. So let's get started and I will be taking you down through my experiences after I finally decided to learn something new.

&nbsp;&nbsp;&nbsp;&nbsp; Machine learning is one among the top disruptive technology for the future and with this good read you will be taking the baby steps to this entirely fascinating world.

&nbsp;&nbsp;&nbsp;&nbsp; As always we need to get the basics right first and then only we can build upon on that. The fundamental break down of the different areas connected with ML(abbreviation for Machine Learning) is provided in the below ven diagram. 

<img alt="ml-ven-diagram" src = "https://dev-to-uploads.s3.amazonaws.com/i/2su3qm7ebd5uiiis6dzz.png" height="200" widht="auto" />

&nbsp;&nbsp;&nbsp;&nbsp; I hope it makes some sense to you, even if not don't worry I didn't understand anything when I saw it for the first time. It shows how ML basically is related to the other big crowd pullers like AI(Artificial intelligence) and DL(Deep Learning). Here we can take DL as sub part of ML and together they both makes a part of the bigger AI. We also have many other areas like NLP,Image Recognition, Data Science which are totally beyond the scope of this discussion.

&nbsp;&nbsp;&nbsp;&nbsp; In the present day world, ML is of great importance and we make use of it solve very complex real life problems. With the help of enhancing computational power this area grows exponentially. We can find these technologies rooting in almost all sectors and sections of human life ranging from the basic smart phones to big spaceships and health, agriculture, military (many more).

&nbsp;&nbsp;&nbsp;&nbsp;ML is different from normal programming where in a computer generates the output for a certain input values according to the program that we feed on to it. But in ML the input and output for a specific scenario are given to the machine and it finds out valuable insights from those information, using which it trains the model based on many of the learning algorithms to predict outputs for an entirely new set of input data.

&nbsp;&nbsp;&nbsp;&nbsp; There are many algorithms to talk about, which we will be covering in future blogs. For this post we are sticking on to pure basics. Let me give you a scenario, consider you and 2 of your friends are going to buy icecream from a shop and the cost of icecream for different number of people are listed in a chart as given below

![icecreacart-problem-visualisation](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/cp6s65tkl9cdg6wweckg.png)

&nbsp;&nbsp;&nbsp;&nbsp;Including you, it requires you to buy 3 icecream and that value is missing on the chart, human mind can easily calculate the rate for 3 icecream and we get Rs.30, this is because of the cognitive and mathematical ability that our mind posses or gains over our life, but computers or machines in itself do not have this ability or intelligence. So we have to make use of various methods which involves step by step learning of the scenario with the help of algorithms. Generally we call it the **Learning algorithm** which enables the machine to learn and help us in prediction or classification. 

&nbsp;&nbsp;&nbsp;&nbsp; From the above case we used a price chart initially which contains all the data we need, it becomes our **Data set**(we have divisions within this data set, just keep in mind). The scenario explained here is a simple case of machine learning techniques called the **Supervised learning**. It is a type of learning algorithm in which the initial learning datasets are provided by a human in order for the machine(program) to learn. Once the model learns via a process called **training** (repetitive learning processes carried over the datasets) helps in predicting a future value which the machine doesn't know. This is mainly by forming certain relationships or patterns in the given input data.


&nbsp;&nbsp;&nbsp;&nbsp;tldr; To summarize, here we have seen a simple example(which you may find strange) of supervised learning. ML is not required for solving this problem but in real life complex problems replaces it. There are numerous learning techniques/methods and algorithms which we may cover in the future. Hope to write more and happy learning!