The Top Reasons People Succeed as a Beginner too In the Machine Learning Industry
Tags: machinelearning, beginners, guide, roadmap
Published: 2022-09-09 18:20:33

Are you really fascinated on the technologies like **Artificial Intelligence** and **Machine Learning**?

Do you wish to showcase Machine learning or AI as your _area of interest_ in your **profile**?

And the most common question, where can you get started?🤔
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> _I would like to share my **personal experience** on how I started working with machine learning. Initially, I was also pondering the same questions. But after I started working in this field, I understood how well ML can help us in our day to day problems._ 

> _This quest to explore helped me in doing **projects that shaped my knowledge** and understanding towards ML and moreover benefitted me in several ways._


Be it any _competition & hackathon_ or a _summer internship_ at prestigious institutes or even _cracking an interview_, all these require a basic knowledge of any of the **emerging techs**. Machine learning will certainly be the best option for you to choose!

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## ML - Precise definition

Machine learning is a subset of artificial intelligence that has an ability to learn automatically without being explicitly programmed.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/mt2w3n1t555ijyxh28c8.jpeg)

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## Why machine learning?
Machine learning is simply not restricted to one or two use cases. It is an exciting technology to learn and has its applications in hundreds of avenues of life.

When I came across this word Machine learning, I thought this will comprise of complex math equations and algorithms which a layman or a sophomore student would find very difficult to understand.

But to my amazement🤩, machine learning was one of the simplest and easiest areas to learn as a beginner. Let me tell you how, in later part of this blog. Machine learning does not require any expensive tools for deployment, all you need is a PC in a good working condition!<br>

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## Beginner's secret
As a beginner, I preferred _reading blogs_ to get familiarized with the concepts and to begin from the scratch. **Blogs** and **tutorials** can serve as a **great resource** for you to pick any specific sub-domain as the field is very vast.

Also, reading the _experiences of many machine learning enthusiasts_ can help you create some perspective with better understanding. You can also take up an online course to learn the basic concepts in ML, there are lots of helpful courses for beginner, so do give it a try!💪

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## Get strong hold over the concepts
Initial stage of mastering any tech is getting a **_strong_** hold over the important concepts and terms. The theoretical aspects will help you better understand and map your learnings that are performed during practical implementation.

Maintaining **short notes** of the concepts learnt will serve you great in the future! So do make a note of what you learn.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/zaxya02sr9r1qsw3l2mv.gif)

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## Learn By applying
Apart from mastering the theoretical aspects, you will certainly have to **apply the machine learning technique or process** that you learn. The application can either be a simple supervised learning model (where you got to do data pre-processing and training prebuilt algorithms as a major task) or even a full-fledged project.

The choice of your application is based on your understanding of the concepts and your confidence to implement it. Also make sure to save this implementation for future reference.

Most importantly, **pick a process and a tool** for implementation. The process itself refers to the methodology or the overall plan and the tool is the ML model(start with the basic one, example: Built in classes of YOLO for object detection)

**Practice on datasets**, there are ample of datasets available over the internet, choose any of it and start the process (For beginners: dataset is a collection of image or textual or even csv data that is used in training the ML model).


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ax8k8wdm3zvvjq9zelne.jpeg)

> _Answering my previous question of why ML is one of the simplest areas to learn, ML has lots of **prebuilt models** and **libraries** that can easily be used in numerous applications._

> _You need NOT go deep into the **math behind** the algorithms to explore each of it, what you need to do is **apply** the ML algorithm first and see how it works. Observe the results, further improvements can be made through analysis of the real code and it's math._

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Try this out, this method has helped me and will certainly help you too!😄

And yeah, if you have read this blog till end, it shows your interest in learning ML and you can definitely fulfill your desire through **consistent learning** and **implementations**.

All the best!