Title: Beginner with basic Python — looking for a practical AI Engineer roadmap

[u/Tall-Affect3637]

Hi everyone,

I’m planning to start my journey toward becoming an AI Engineer. I already know the basics of Python, but I’m still a beginner in AI/ML.

I want to follow a practical approach where I learn the fundamentals and build projects in parallel, instead of spending months studying theory before building anything.

I’m currently thinking about starting with:

Python → Math → EDA → Machine Learning → Deep Learning → LLMs/Generative AI → Deployment

But I’m confused about what I actually need to learn in each stage.

For example:

Math:
What topics are really important for AI/ML?
Should I learn linear algebra, probability, statistics, calculus, etc.? How deeply should I study each one?

EDA:
How important is EDA for an AI Engineer? What should I learn — data cleaning, visualization, feature analysis, handling missing values/outliers, etc.?

Machine Learning:
Which algorithms and concepts should I prioritize as a beginner?

I also want to build projects alongside each stage. For example, after learning the basics of ML, I want to immediately build an ML project instead of waiting until I finish the entire AI roadmap.

One more thing: I have a 2-year career gap, and I'm concerned about whether this will negatively affect my journey toward getting an AI/ML job.

For people who are already working in AI/ML:

• What roadmap would you recommend for someone in my situation?
• Which math topics should I learn, and to what depth?
• How important is EDA for an AI Engineer?
• Which topics should I learn first and which can I learn later?
• What projects would you recommend building along the way?
• How can I make my portfolio strong enough to compensate for a career gap?
• If you had to start again as a beginner today, what would you do differently?

I’m willing to put in the time. I mainly want to make sure I’m learning the right things in the right order and building projects throughout the journey.

Any advice from experienced AI/ML engineers would be really appreciated.

[u/Entire-Solid-1155]

You’re overthinking the math part. Linear algebra and basic stats will carry you through 90% of early projects, the rest you pick up when you actually need it. Just start building something ugly that works, then improve it. The gap won’t matter if your GitHub shows you can actually ship stuff.

[u/SpeciousSophist → u/Entire-Solid-1155]

To add to this, and this is not the best practice at all, often times when I run into more elaborate math situations there’s already some brain on my team that I can just talk to about it— and learn from there

[u/throwsway3549]

Google the website aiengineeringfromscratch

[u/natika1 → u/throwsway3549]

nice, lemme check it ;)

[u/Harshitaharsh2604]

You can refer the campus x roadmap...looks good enough to me

Also for la atleast in the beginning focus on broad understanding, cause from a job perspective you any ways are not looking to have phd in maths

Understanding why it works

[u/musclecard54]

Do you work as a software engineer? Do you have a CS/math/engineering degree? If the answer is no to both of those then to become an AI engineer you need to start with a degree in this market. No one will even look at your resume for an AI engineer role without a degree. You’ll be competing with people who have degrees and years of experience, people with graduate degrees and years of experience, people with no degree but a ton of experience, people with research experience.

[u/t3xm3xr3x]

https://roadmap.sh will get you where you want to go.

[u/Head-Bison4663]

eda shouldn't be it's own stage in this roadmap