AI is the buzzword everyone wants to know about, but do you really have what it takes to dive deeper into the subject and really understand the nitty gritty and nuances of Artificial Intelligence.

So, it has been a while since I last posted and I made something today that is worth writing about. I really hope it helps you.

I have already spent years in this field. This field is dynamic and keeps on expanding. New technologies and models are being added each month. So, I wanted to revise the concepts as well as learn the newer additions into this field.

Here is my Roadmap for learning Artificial Intelligence. This roadmap encompasses artificial intelligence, machine learning, deep learning, domains like natural language processing, computer vision, machine perception, Tools and libraries, Mathematics for ML & AI, as well as projects.

I will personally be following this roadmap in 2025 posting about each phase in detail as I progress through my Study Challenge: Studying One thousand hours of AI in 2025

AI Roadmap 2025

Phase 1: Mathematics and Statistics

One needs solid understanding of foundational topics like mathematics and statistics to ever dream to be good at Artificial Intelligence. Surface Level understanding doesn’t cut through the noise that we have in this world. World is full of people calling themselves “experts” with little to no understanding of the deeper level of Artificial Intelligence concepts and yes that includes mathematics.

My aim is not to scare you but to make you aware what is needed to be good at this. Start with Basics and develop day by day. Try to build understanding of concepts rather than just completing the subject in certain amount of hours. It is okay to take 30 hours for a topic, if you need 30 hours. But you need to do it so well that you can easily apply the topic whenever and wherever needed.

Phase 2: Coding

Here is the thing, I think coding is one of the fundamental thing a software developer or a normal person should know of. Coding helps you in Problem Solving. If you are just getting into the field of AI, even then I would recommend you to get really good at coding in the initial phase because until when are you going to ask ChatGPT to code for you? Someday there will be a time and idea in your head that ChatGPT won’t be able to code for you. At that time you will need real skill and it will be very frustrating for you to not let that idea turn into reality because of your incompetence. So here is my advice, invest time in coding, be good at it - you don’t need to be best in the world, just enough to be able to turn ideas into reality. Yes, you are allowed to let ChatGPT fix your code.

Phase 3: AI Concepts

What is the use of learning AI tools if you don’t really know what it is? How it is useful for you? what kinds of things it can do for you? What products and domains you can apply it in?

Invest time in learning about the theoretical concepts rather than jumping right into coding and projects because you would want a fruitful project for yourself, and not some mediocre project anyone can do.

Phase 4: ML Concepts

These are the concepts I learned in the M.Tech. Yes, I did my specialization in Artificial Intelligence, so I know what I am talking about. These are the basic things you need to know about.

Also don’t you dare call yourself “expert” after just knowing this much. Who are you trying to fool? You know you have a long way to go before you can get close to calling yourself that.

Phase 5: DL Concepts

People think learning to use ML libraries means that they know the subject. Well, they don’t. You need to get into the nitty-gritties of the each deep learning model and their working mechanism. Don’t skip this part. It’s a necessity and not optional. You need to know the algorithm really well and not just the outlook of the algorithm. Spend a lot of time in this phase. It will help you later.

Phase 6: ML & DL Implementations

Now that you know all the theoretical concepts of AI, ML and DL as well as you are well equipped in coding, this is the time to use these skills to good use. Implement the algorithms you have learned till now using libraries and then from scratch. Implementing ML algorithms from scratch will teach you concepts you didn’t even think was being used.

Learn to visualize the data. Focus on data engineering in this phase because it will be something that will be useful in industry job.

Phase 7: Projects and Deployments

Now that you have good enough experience with ML-AI algorithms, you can move forward to building your projects. I would recommend you start with some already made project by someone else and re-doing it by yourself so that whenever you get stuck you can get help from that person. After that you can go to kaggle and do one project from there. I think if you have done these two projects with your whole brain and not heart, you can do your own projects from now on.

Phase 8: NLP

These are the topics I think is needed to be learned in NLP but you can add more topics if you want.

Phase 9: Computer Vision

Computer Vision includes a lot more things than mentioned here but I wanted to limit to the ones that I find interesting, and you can explore more.

Phase 10: Machine Perception

Machine Perception is the topic that just makes sense to me. It is basically how machines perceives the world. We really need to make better algorithms for this. And this is the topic I feel like is going to be trending in the later part of this decade.

How to customize the roadmap as per you?

Definitely, there will be a time you will find yourself in the blackhole of finding new roadmap each week and switching between content and changing your goals. We have all been there and you will be there if you haven’t yet. So few things you would like to keep in mind while going through any roadmap, not just mine, are:

• What is my goal in life and does this align with that?

• Is this something I just don’t know and that’s why I am finding this interesting, or do I really need to learn this?

• Do I have time to learn this?

• Can I incorporate it with the amount of hours I have to study as per my lifestyle?

There is no point having 10 different amazing goals to achieve and achieving nothing at all. It’s better to pick a few things and do them well.

I hope that this roadmap is what helps you. You don’t have to do the entire roadmap in a year.

You can pick one or two phases of the roadmap and target them this year. The goal is to do them well and not just for the sake of completing them. Make sure you don’t stress too much and comfortably study each day and eventually you will build your skillset. Good luck!!!

After-Words

Let’s not be mediocre and learn about Artificial Intelligence in-depth.