How to Build a Career in AI and Machine Learning as a Student

If you want to learn about how you can build a career in AI and ML as a student then you are at the right place.

Hello everyone, in this complete guide where we will be discussing everything in detail. So, make sure to stay connected.

Artificial Intelligence (AI) and Machine Learning (ML) are no longer just two out of the context words. These technologies are revolutionizing every industry whether it’s big or small.

Nowadays there is a huge demand for these fields, which means there is an opportunity to get a job in these fields. If you are a student and want to stay ahead of others then you can consider taking a look at these fields. Mastering AI/ML can be helpful and will make it easier for you to land a job.

This guide contains everything that you need to know about starting with AI and ML. At the end of this article we have linked the complete roadmap guide, so make sure to read till the end.

So, without further ado let’s get started.

What Are AI and ML?

Artificial Intelligence (AI) refers to the machines that are designed to perform tasks that typically require human intelligence such as learning, reasoning, and problem-solving.

Machine Learning (ML), on the other hand, is a subset of AI, it focuses on algorithms that enable systems to improve their performance over time without any explicit programming.

AI and ML are transforming various industries at a very fast speed by enabling more accurate decision-making, automating complex tasks, and solving problems that were previously difficult to solve. These technologies are already making a significant impact in various sectors like healthcare, finance, education, etc. 

Why Pursue a Career in AI/ML?

The demand for AI/ML professionals is growing at a fast speed. Careers like Data Scientist, Machine Learning Engineer, and AI Research Scientist rank among the top upcoming jobs globally.

These job roles will help you to be financially stable as well as help to do more innovations that will bring a positive impact in the world. 

Some of the popular career paths in AI/ML are:

• Data scientist: collects, processes, analyses the data and finally extracts useful insights from the data. By useful insights, we mean the information that will be useful to the company. By which they can help their users in the best way or help them to make their company better. For more information about this field, you can watch the video on YouTube by Apna College                                                                  
• AI Engineer: Develops AI-powered solutions for real-world applications.
• ML Specialist: a professional who specializes in using machine learning algorithms and models that are used to solve complex data-driven problems. Their primary job is to apply statistical models, data analysis, and computational algorithms to make predictions by using the data. 

Roadmap to Build a Career in AI/ML

If we talk about the degree then we can say that a degree always helps whether you are a mathematics graduate or a computer science graduate. But if you want to go into the field of AI/ML then we can say that you don’t need a degree.

Let’s discuss the things that you need to learn.

Learn programming language:

To learn machine learning you have to learn a programming language, you can start with Python, an easy-to-learn and understand language and also the most widely used programming language in this field.

Python is among the top 3 programming languages in the world (stackoverflow) and is used by more than 8 million developers. Even Google, NASA, and Netflix are using this language.

In Python you have to learn about variables and data types, control flow, functions and modules, file I/O, Exception handling, regular expressions, OOPS, Generators and iterators, Decorators, and web scraping.

To learn all these concepts you can use the free resources provided on YouTube.

There are various packages available like Tensorflow, keras, skikit learn, etc. and then you can choose either Tensorflow or Pytorch to learn machine learning.

These libraries will help you to implement the machine learning algorithms.

Learn Numpy and pandas:

You should learn the basics of both Numpy and Pandas as well.

To learn numpy you can start with the “Numpy Quickstart”. You don’t need to go in-depth about it, just learn the basics and move to the next things.

After this, you can learn about the Pandas. You should be able to do basic things like if you are given CSV data and how you can manipulate it.

To learn pandas you can watch the videos on YouTube or you can check out the notes that we have provided on our website.

Learn statistics and Linear Algebra:

Having a good knowledge of statistics and Linear Algebra  will be needed to be good at AI and ML.

You can learn about these on YouTube or you can take a look at the Notes by Queen Marry University of London.

These notes are just amazing and will help you to understand all the concepts in depth and easily.

After this, you would need to learn about probability as AI works on these concepts. To learn probability you can check this book called “Probability and Statistics in Engineering” by William Hines. 

This is an amazing book, we don’t recommend you to learn this book cover to cover just learn the important topics and refer to this book when needed.

After this make sure to take a look at Discrete Mathematics as well. You don’t need to go in-depth just take the basic knowledge.

Learn Data Visualization:

Data visualization is the graphical representation of information and data using visual elements like charts, graphs, maps, and infographics. It helps simplify complex datasets by displaying trends, patterns, and insights in a way that's easy to understand for everyone.

For data visualization, we recommend you to learn Tableau and Power Bi but one thing is that these are paid. To learn these you can use free resources on YouTube.

After all this you can start with the basics of ML:

Learn Basics of ML:

You can start with Linear Regression or gradient descent. Learn all the algorithms of supervised learning and unsupervised learning.

There is this book called “Mining of Massive Datasets” by Jure Leskovec, in this book, you can learn about supervised and unsupervised algorithms easily.

Then you can learn about Clustering and make sure to implement this algorithm using sciket Learn.

After all these things you will find new things by yourself, like what to learn next. So, keep exploring the things and enjoy the process.

Implementation:

Learning things by doing makes it easier for you to understand things properly and in-depth. So, whatever you learn make sure to implement it alongside.

Practical experience is important:

• AI/ML Projects: You can start with basic projects like chatbot development or image recognition. And then with time progress to advanced challenges in natural language processing or predictive modeling.
• Competitions and Hackathons: start participating in competitions on platforms like Kaggle to test your skills.
• Internships: Try to get an internship to gain real-world experience.

Build a Portfolio and Network:

If you are learning things or you make projects and nobody knows about it, then what’s the point of doing it? You have to tell the world about your existence and also about your skills. So, make sure to follow the following things:

• Share Your Work: You can use GitHub to upload your projects so that anyone can see them.
• Write blogs posts: Publish blog posts on AI topics to tell the recruiters about your expertise. You can either write on your website or Medium.com as well.
• Join the Community: Connect with AI professionals on LinkedIn. You can also engage in online forums such as Reddit’s r/MachineLearning.

Final Thoughts

What does it require to pursue a career in AI/ML? The answer to this question would be it requires commitment, continuous learning, and a strategic approach. If you’re just starting or looking to get more knowledge about AI/ML, then these steps will guide you toward success.

That was it for this article, if you found it helpful then make sure to share it with a friend of yours. Also if you have any suggestions for us then do let us know in the comment box.

All the resources mentioned in this article are given below, go and take a look if you want to learn about AI/ML.

With this thank you for reading this article. Cheers

Roadmap:

1. Roadmap by Code with harry click here
2. Roadmap by Genie Ashwani click here