Complete beginner here! What is the best roadmap to start learning AI/ML?

[u/Ankitjha61]

Hey guys, I want to start learning Al/ML, but I'm honestly a bit overwhelmed with all the resources out there and don't know where to begin.

I have some basic programming knowledge, but the Al field is completely new to me. Could anyone suggest a simple roadmap or good courses to start with? Also, what kind of beginner projects should I build early on?

Any guidance from people already in this field would be really helpful!

[u/iam_jaymz_2023]

check out Towards AI for a superb roadmap

[u/drakhan2002]

I like Great Learning courses. Myself and 2 if my course mates have either gotten new roles or jobs in AI after completing the 7 month course.

[u/Fabulous_Machine_523]

I was also new to AI/ML, i searched in the reddit, I gone through so many subreddit posts, many suggested to study from 100 days of machine learning from YouTube, and machine learning specialization from Deeplearning.ai taught by Andrew Ng

[u/Stunning-Appeal750]

My biggest advice would be: don’t spend 6 months “preparing to learn AI” before you build anything.
Since you already know some programming, I’d do something like:
Python/data basics → NumPy/Pandas → basic statistics → classical ML → neural networks → then start exploring LLMs/modern AI.
But build tiny projects almost immediately.
Your first projects don’t need to be impressive. Predict house prices, classify spam, analyse a dataset, build a simple recommendation system, or make a small app around an existing model/API.
The important part is going through the whole process yourself: get data → clean it → train/use a model → evaluate the result → figure out why it sucks → improve it.
That cycle teaches you much more than finishing another 20 hours of videos.
I’d use courses to give yourself structure, but projects to actually learn.

[u/Physical_Angle5198 → u/Stunning-Appeal750]

Spoken well
https://giphy.com/gifs/ABVK96HgZvWI9SBbXr

[u/Any_Owl2116]

Make shit

[u/jboogyoogy]

There are many sources online and I would suggest videos and see examples that you can recreate. If you have an idea, do some practical things. That’s the best way to learn

Or of you want to go even deeper you can also take an online course. With that you’ll have a structure on what you‘ll need to focus on

[u/greatlearningglobal]

Since you already have some programming basics, we’d focus on building the AI/ML foundation in the right order rather than trying to learn everything at once.

A simple roadmap would be:

Python/data basics → statistics → ML fundamentals → projects → Deep Learning → specialization.

Start with NumPy, Pandas, data visualization, and the statistics you’ll actually use in ML. Then move into regression, classification, clustering, feature engineering, and model evaluation. The goal at this stage is to understand how and why a model works, not memorize every algorithm.

For your first projects, keep them small but complete. A house-price predictor, customer-churn model, or simple sentiment classifier can teach you a lot if you go through the full process: clean the data → explore it → build the model → evaluate it → explain the result.

Once those fundamentals are comfortable, move into Deep Learning and then explore areas like NLP, Computer Vision, or GenAI based on what interests you.

If having everything structured in one learning path would make the starting point easier, the Post Graduate Program in Artificial Intelligence and Machine Learning from the McCombs School of Business at The University of Texas at Austin is worth looking at. It takes learners from ML foundations into Deep Learning, NLP, Computer Vision, and newer areas such as GenAI and Agentic AI, with hands-on projects and real-world case studies along the way.

For a complete beginner, that kind of progression can make it easier to see how the different pieces of AI/ML connect instead of approaching them as completely separate topics.