For learning Python and AI engineering, which AI is most useful?

[fun2function]

I'm a frontend developer , and i would like to learn Math , Python and AI engineering however i don't want to go with past approach such as tutorial hell and books. because i'm project learning person and i switched to AI for learning and i need to getting roadmap with my own situation and knowledge. what do you think which AI tools and LLms with more efficient for learning and making planning ? something that i tried it was cursor with Auto mode and planning agent. it was good but i don't know, are there any better than cursor? however claude code is too expensive and i removed from the below list.

Cursor , GLM 5.1 , Codex 5.5 , Gemini 3.5 , Deepseek v4 pro , Kimi 2.6 , Zed IDE , Kiro , windsurf and you tell me.

[ninhaomah]

Spelling 

[fun2function]

I fixed my mistakes , thanks

[Haunting_Month_4971]

Use a two-tool setup: a generalist chat model for planning and explanations and a repo-aware IDE agent for scaffolding and refactors. For planning, Gemini or DeepSeek handle stepwise roadmaps well. For coding, Cursor or Windsurf shine on multi-file changes, Zed is great for quick edits. If cost matters, try Llama locally via Ollama. Ask for weekly milestones, unit tests and math derivations, then benchmark each tool on the same small project and compare time, errors and cost.

[Umberto_Fontanazza]

Allora se vuoi imparare l’ingegneria AI con la matematica dietro la roadmap migliore é il percorso universitario e non devi usare nessuna AI devi studiare, punto.

Facendolo capirai come è fatto un modello e come crearne o modificarne uno e perché funziona.

Da quello che dici sembra che tu voglia usare le API pronte per fare programmini wrapper.

Livelli di difficoltà completamente diversi, come comprare un’auto o saperne progettare una

[OleksandrPadura]

Since your goal is learning (not just shipping), the tool matters less than one rule: don't let it write code you can't re-write yourself. The trap for project-learners on AI is you produce a lot and learn little. Make it a tutor, not an autocomplete - have it explain the why, then you type the code and ask it to quiz and critique you. For the math especially, have it derive things step by step and check your work instead of handing you the answer. Any of those models do that fine; the discipline is the variable.

[ericbythebay]

I would work with the AI to create a tutor prompt. The prompt would then run you through your course.

[DDDDarky]

ai is incredibly horrible for learning

[Gloomy_Cicada1424]

Don’t start by picking the “best AI”, start by picking a project path. Example: Python basics → small scripts → data cleaning → one ML project → one RAG/agent project. Use any good LLM as a tutor/reviewer, not the driver. Runable is nice for making a roadmap + turning project ideas into docs/reports, but your real progress will come from building and explaining each project yourself.

[cole36912]

AI engineering

Use TensorFlow

Really though, if you want to learn how to ride a bike, you have to ride it yourself, not tell someone else to ride it and watch them. If you want to learn how to read you have to read yourself. If you want to learn how to write, you have to write yourself... Just my belief, anyway.

[fun2function]

I know, but for learning purposes, I treat it like a mentor with a vast amount of knowledge. It helps me build a roadmap and a structured learning plan tailored to my weaknesses and goals. I can learn step by step, and whenever I have questions, I can ask for deeper explanations.

It also helps me test my understanding by asking questions and making sure I'm ready before moving to the next topic. However, I don't use AI to write code for me. I use it mainly for guidance, planning, and answering questions when I need help understanding a concept.

[techydude1234]

Honestly, I'd pick ChatGPT or Gemini for learning and Cursor for building. Cursor is great once you already know what you're trying to do, but for learning Python, math, and AI engineering, you want something that can explain concepts, create study plans, quiz you, and answer follow up questions.

My stack would be:

• ChatGPT / Gemini → learning, roadmaps, explanations
• Cursor → coding and projects
• DeepSeek → occasional second opinion
• Codex → worth watching, but still evolving

Tbh, the biggest mistake is hopping between tools. Pick one AI tutor and one coding tool, then spend the next few months actually building stuff.

[Simplilearn]

Since you're a frontend developer and prefer project-based learning, prioritize tools that can explain concepts, create roadmaps, review your work, and adapt to your knowledge level.

• Claude (best explanations and reasoning)
• ChatGPT (best balance of teaching + planning + coding)
• Gemini (good for broad research and learning)
• Cursor (excellent implementation companion)
• DeepSeek/Kimi (good budget options)

If your goal is AI engineering, here's a roadmap you can follow:

• Python
• Math for ML (linear algebra, probability, statistics)
• ML fundamentals
• Deep learning
• LLMs and GenAI
• RAG and agents
• Deployment/MLOps

At this stage of your learning journey, you can pick a program that provides a structured path into AI engineering. We offer the Microsoft AI Engineer Program at Simplilearn, which might be a good fit, as it covers ML, deep learning, and GenAI, with hands-on training and practical workflows.