大家好，
我于2024年获得计算机科学学位，并希望在AI/ML领域培养真正的技能。我看到许多大学在网上提供免费的AI课程，但我无法分辨哪些才值得花时间。
我的背景：
我在寻找什么：
我的目标是成为一名 AI/ML 工程师 / 从事研究 / 以后读博]。我的预算是[低]。
先谢谢大家。非常感谢任何个人经验分享！
Start with a solid Python and math refresher, then take a well-known introductory machine learning course before moving on to deep learning and a focused specialization in the area you want to work in. For a degree, check accreditation, total fees, exam costs, and whether employers in your target field actually recognize the program before you enroll. A low-cost accredited online master’s can be a good long-term investment, but it is usually worth it only if you also build projects alongside it. For most AI engineer roles, hiring managers weigh a clear portfolio, clean code, and practical results more heavily than the name of a degree alone. Pick one learning path, finish a small end-to-end project every few weeks, and use that work to show what you can do.
I would not rush into a full degree first. Start with one solid intro machine learning course, finish it properly, and build two small projects around things you actually enjoy, like a simple classifier and a small chatbot or data-cleaning tool. That gives you something to talk about in interviews much faster than collecting certificates. For a degree, i would check the total cost, exam fees, and whether employers in your target area recognise it, before signing up. A low-cost accredited option can make sense later, especially if you want research or a PhD, but for most engineer roles a clear portfolio plus steady practice usually matters more.
𝐀𝐈/𝐌𝐋 𝐋𝐄𝐀𝐑𝐍𝐈𝐍𝐆 𝐏𝐀𝐓𝐇 𝐅𝐎𝐑 𝐂𝐒 𝐆𝐑𝐀𝐃𝐒From 2024 Grad → AI/ML Engineer
You’re asking the right questions. Good news: you don’t need to spend much (or anything) to build real AI/ML skills.
THE COURSE ORDER THAT ACTUALLY WORKS| # | Course | Why | |
|---|---|---|---|
| 1 | Elements of AI | Gentle on-ramp, no coding needed | |
| 2 | Harvard CS50 AI | Search, ML, neural nets, 7 projects | |
| 3 | MIT 6.S191 Deep Learning | Updated yearly, LLMs + agentic AI | |
| 4 | MIT 6.036 ML | Full-semester ML foundation | |
| 5 | Stanford CS229 ML | Graduate-level math, Andrew Ng | |
| 6 | Stanford CS224N NLP | NLP specialization, advanced |
DO: Take them in this exact order, each builds on the last
DON’T: Jump to CS229 without foundations, the math will eat you alive
DEGREES THAT ARE WORTH THE MONEY| Program | Total Cost | Hidden Costs | |
|---|---|---|---|
| Georgia Tech OMSCS | Under $7,000 for full degree | Fees jumped to $531/semester in Fall 2026 | |
| University of the People | ~$6,460 bachelor, ~$5,260-5,460 master’s | $60 app fee + $160-450/course assessment fees |
DO: Pick OMSCS if budget allows, stronger AI/ML credential
DON’T: Assume UoPeople is truly “free,” per-course fees add up fast
DOES A MASTER’S ACTUALLY MATTER?| Role | Master’s Impact | Matters More |
|---|---|---|
| AI/ML Engineer (industry) | Helpful, not required | Projects, GitHub portfolio, deployed work |
| ML Research (labs) | Often expected | Publications, strong math foundation |
| PhD track | Usually required | Research exp, publications, letters |
DO: Build 2-3 substantial deployed projects alongside coursework
DO: Contribute to open-source AI repos, signals real skill
DON’T: Treat a master’s as a shortcut around building a portfolio
YOUR 9-MONTH ROADMAP
Months 1-3: CS50 AI + MIT 6.S191
Months 4-6: MIT 6.036 + start project
Months 7-9: CS229 + open-source repo
Ongoing: Apply to OMSCS if budget fits
Parallel: Keep building projects
Free courses get you the knowledge. Projects get you the job. A master’s gets you the credential. Stack all three and you’re genuinely competitive.
!