I’m from a non-technical background(UPSC CSE aspirant) with no prior coding experience. I’d like a clear, step-by-step roadmap to transition into Applied AI Engineering/AI & ML. Specifically:
I have no background in math, Python, or any technical field and want to start from scratch, beginning with Python.
• Prerequisites (math, programming, tools) and how to build them from scratch
• Recommended courses and a suggested learning sequence
• How to gain experience (internships, open-source, freelance) without prior tech roles
• Certifications or credentials that are actually valued by employers
If you’ve made a similar transition, please share your timeline, resources you used, and what you would do differently. A 3–6–12 month plan would be very helpful. Thank you!
Copilot is my Coach—I used it to help me build my roadmap, and maybe this will help you too. If you’re curious about how much math you really need for AI/ML, I found ChemCoder’s videos on the internet helpful for breaking it down. They’re not promotional—just solid insights from someone who’s been through the learning curve.
Copilot says:
“That’s a fantastic and refreshingly honest request—clear, focused, and full of ambition. Coming from a UPSC CSE background, you already have discipline, analytical thinking, and a strong work ethic—traits that translate beautifully into AI/ML. Let’s build a step-by-step roadmap tailored for someone starting from absolute zero, with a focus on Applied AI Engineering.”
Phase 0: Mindset & Strategy (Week 1–2)
Before diving in, set your foundation:
Define your goal: Applied AI Engineer (focus on building real-world AI systems, not just theory)
Time commitment: 15–20 hours/week is ideal
Tool setup: Install Anaconda, VS Code, and create a GitHub account
I’m just starting out in AI myself—but I bring over 25 years of experience from roles at Motorola and as a programmer/analyst in my early career. If you’re new to AI, here’s one thing I’ve learned:
Mathematics is the foundation of Machine Learning and Data Science.
It’s not just theory—it’s the language AI speaks. To understand how models work, why they behave the way they do, and how to improve them, you need a solid grasp of math.
I highly recommend checking out ChemCoder’s videos on YouTube. He breaks down the essential math topics and shows how they connect directly to AI and ML. His content is clear, practical, and beginner-friendly.
Start there, and build your foundation step by step. You’re not alone in this journey.
Hi. Sorry for the disappointment but without math you will never understand AI. AI is about linear algebra, calculus, and statistics. And writing algorithms for any programming task requires deductive logic. It would take you several years to learn the math.
I am in a similar position. I have watched many YouTube videos on AI. Look into Claude Code and Windsurf. I am very math-challenged but I managed to Vibe Code a working application using those two resources that trained on a low-resource Indigenous language for TTS and MFA. What that demonstrated to me is that I really want to have a better understanding of the ML learning process. I need to approach this with Context Engineering intent as opposed to Vibe Coding. I also need to learn Python.
Many opportunities are opening up in AI, it is democratizing the field. Lean heavily on AI to explain concept to you at the level you need, it is only getting better and better. Check out Dynamous AI (www.dynamous.ai) and think about taking the AI Agent Mastery Course, it is the best AI learning resource I have found yet.
Thanks g15713 for the ChemCoder’s recommendation, that is exactly what I need. I really want to understand math but it has been very difficult journey for me.
If you do not understand the fundamentals, then your ability to recognize when the AI does something wrong will be very limited, as will your ability to fix the issue.
= = = =
Many people know how to drive a car. Fewer know how to fix one, and even fewer can build a new kind of car from scratch.
It’s up to you to decide if you’re a user, a fixer, or a designer.
All are valid choices, but they also have different risks and rewards.
By ‘non-technical background’ I mean I want to learn from scratch. It does not mean I want to build a career in AI engineering without learning mathematics, but I want to know where to learn maths from.
A good place to start would be Machine learning. DeeplearningAI has a course on Coursera. It goes over the code and the math. You don’t need a lot of math to get started, but it does help when it comes to understanding what the algorithm is doing, because it is an equation or a combination of them. Coursera: Machine Learning Specialization. Machine Learning is the foundation of AI. It leads into Deep Learning, which is a more robust and complex level of Machine Learning. I’m excited for you! Hope this helps.
I went the University route into tech (CS degree) and applied for internships from the start of my second year. However, there are a lot of resources available.
Roadmap.sh is a great resource for exactly this! I’ve linked some which I think would suit your desires. They are not courses, but step-by-step roadmaps for every skill you need to learn. Start-to-Finish. They are human made by people who work in those fields.
A lot of steps also link out to courses they recommend.
Best of Luck,
DevRory
P.S. as far as certifications go, I’ve interviewed some employers and some care some don’t. IBM machine learning is generally good. I would recommend a portfolio you can demonstrate (of projects) over certifications.
This question recurs regularly over the 8 years I have been involved with deep learning courses here. Thought I would share this current job posting from OpenAI that I looked at last night…
Interesting to me that no education, degree, or certifications are mentioned. Also that experience with engineering any Internet scale solutions seems as important as anything AI-specific. To @TMosh ’s point above, there are many different job roles in an ‘AI’ company, many of which don’t involve or require one to be the next Sutskever, Karpathy, or PJ Reddie. Suggest ground your roadmap in your personal strengths and interests, not just what today’s job market seems to want (because that is going to be different by the time you get there anyway)
Phase 0: Mindset & Strategy (Week 1–2)
Phase 1: Python & Programming Fundamentals (Month 1–3)
What to Learn
Recommended Courses
Tools to Practice
Phase 2: Math for ML (Month 3–6)
Phase 3: Core ML & Applied AI (Month 6–12)
Phase 4: Projects, Experience & Career Prep (Month 9–12)
Certifications That Matter
Suggested Timeline
Bonus Tips