Machine Learning 101: What You Need to Know
Tags: machinelearning, beginners, tutorial, ai
Published: 2024-05-25 19:48:32

## Introduction
Welcome 👋 to this blog. Have you heard about the **Chat GPT** or have you used **Chat GPT** for any of your tasks? If your answer is yes have you ever wondered how these technologies are working internally? Why these technologies are popular nowadays? Then you are now in the correct place this blog will cover the basics of machine learning which is responsible for these kinds of technology. These technologies are the outcome of Machine Learning.

*Note: In this blog, we will not talk much about the mathematics of the algorithms to keep it simple so that everyone must get the basic intuition of the algorithms*

## Machine Learning
Let's break down the words **Machine Learning = Machine + Learning**, A machine will learn to perform a task that it has not been explicitly programmed.
*or*
The process of training a machine using data so that it will behave according to the provided new data or an external new environment.

![machine learning and artificial intelligence venn diagram](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/8spvqs1hzk83u5juxyka.png)

We can also say that *Machine Learning* is a subset of Artificial intelligence.

## Types of Machine Learning Algorithms

We can generally classify the machine learning algorithms into 3 types:

1. Supervised learning algorithms
2. Unsupervised learning algorithms
3. Reinforcement learning algorithms

Let's dive into each of the algorithm types in detail.

## Supervised Learning Algorithms

In layman's terms, we can say that this is a class of algorithms in which the \*supervision\* of the data is required in the process of training the model. Let's take an example for more clarification, suppose we have a dataset:

```
| Size (sq ft) | Area            | No. of Kitchen | Price ($) |
|--------------|-----------------|-----------------|-----------|
| 1200         | Downtown        | 2               | 300,000   |
| 1500         | Suburban        | 3               | 350,000   |
| 800          | City Center     | 1               | 200,000   |
| 2000         | Suburban        | 4               | 450,000   |
| 950          | Downtown        | 2               | 250,000   |
| 1800         | Rural           | 3               | 320,000   |
| 1600         | City Center     | 3               | 380,000   |
| 1100         | Rural           | 2               | 210,000   |
| 1300         | Suburban        | 3               | 300,000   |
| 1400         | Downtown        | 2               | 330,000   |
```

In this example, we are trying to make the house price prediction kind of model to predict the price of the house given the *size, area, no. of Kitchen*.

If you see the dataset carefully you will notice that in each row we have a notion of \*supervision\* with the **price** column. Each input in the dataset is mapped to the price giving us a notion of supervised that's why this algorithm is called the Supervised learning algorithm.

## Unsupervised Learning Algorithms

In this type of learning algorithm, we don't need our learning model to be supervised by the dataset. Our model will automatically learn the meaningful pattern & information from the data. One common example is the segmentation of news articles, where the algorithm groups articles into categories such as politics, sports, and technology without pre-labelled data.

## Reinforcement Learning Algorithms

Reinforcement learning algorithms learn by interacting with an environment, making decisions, and receiving feedback in the form of rewards or penalties. The goal is for the model to learn a strategy that maximizes the incremental reward over time. A classic example is training a model to play a game, where the algorithm improves its performance by learning from the outcomes of its actions, such as winning or losing.

## 🎉 Conclusion
You have learned the basics of machine learning algorithms. You now understand what machine learning is, how it can be classified, and the significance of each classification.

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