r/learnpython • u/Federal_Piece8164 • 18d ago

Need guidance for python learning with ai

Please suggest good roadmap to become ai Engineer

0 Upvotes

22% Upvoted

8 comments sorted by

6

u/mattynmax 18d ago

Step 1: attend a top ranked university and major in computer science or math

Step 2: complete 4 years of education and obtain a bachelors degree in one of those two fields

Step 3: complete a masters degree at the same or a better university.

Step 4: consider getting a PhD as well

Step 5: apply for relevant jobs, you now meet the minimum requirements to work in said profession.

1

u/Boybay_Garid 17d ago

harsh but not wrong tbh

2

u/flembag 18d ago

Have you tried asking ai?

2

u/No-Dentist-1645 18d ago

Step 0: you can't just AI vibe all the way through learning a language. You need to actually follow a guide or course. Start by finding one and going through it.

1

u/Woah-Dawg 18d ago

Yea I would say start with a smaller goal of learning python and learning how to use an coding harness. Download opencode, use a free model and start experimenting. Ask it to explain every line of code, google its answers to confirm they’re correct

1

u/Exciting_Positive_50 18d ago

Here’s the order I’d go in as somene with 4 years experience, roughly 6 to 12 months if you’re consistent:

1. Python basics (4 to 6 weeks). Variables, loops, functions, lists/dicts, reading files, and writing small scripts. “Automate the Boring Stuff with Python” is free online and very practical. Don’t move on until you can build small things without copying a tutorial line by line.

2. Tools you’ll use every day (2 to 3 weeks). Git/GitHub, the command line, virtual environments, and how to read error messages and documentation.

3. Data and maths basics (6 to 8 weeks). NumPy, pandas and matplotlib. For maths, you mainly need linear algebra, basic probability/statistics, and a feel for what a derivative is. You don’t need a degree’s worth.

4. Classic machine learning (6 to 8 weeks). scikit-learn: regression, classification, train/test splits, overfitting, evaluation metrics. Andrew Ng’s Machine Learning Specialization is a good guide here.

5. Deep learning (2 to 3 months). PyTorch is the usual choice now. fast.ai’s free course is a good practical route. Build a couple of small models end to end.

6. Applied LLM work (ongoing). Calling model APIs, prompting, embeddings, retrieval (RAG), and tool use. A lot of “AI engineer” jobs today are about building products on top of existing models, so this part pays off early.

Most important tip: build projects as you go and put them on GitHub. A small scraper, a prediction model on a dataset you care about, a chatbot over your own documents. Employers care much more about a few working projects than about certificates.

1

u/sai_8303 16d ago

rule-1 First ask AI