By Quick answer: the ML Specialization or the Deep Learning Specialization: which should I choose in 2027?
- Choose the ML Specialization — you're new to machine learning.
- Choose the Deep Learning Specialization — you've finished ML foundations.
- Verdict — ML Specialization first, then Deep Learning.
- Before you pay — take a free practice test.
Key takeaways
- Choose the ML Specialization if you're new to machine learning.
- Choose the Deep Learning Specialization if you've finished ML foundations.
- Check job posts in your target market before deciding.
the ML Specialization and the Deep Learning Specialization are often compared, but they suit different people. Here's a side-by-side comparison and a clear way to choose.
The options at a glance
Ranked for this reader. Costs and formats come from each issuer's own page, checked on 3 November 2026; prices change, so confirm before you book.
| # | Certification | Cost | Time | Prove it free |
|---|---|---|---|---|
| 1 | Machine Learning SpecializationDeepLearning.AI & Stanford / Coursera | $49/mo Coursera plan1 | 2 months at 10 hours a week | Free test |
| 2 | Deep Learning SpecializationDeepLearning.AI / Coursera | Coursera plan2 | 3 months at 10 hours a week | Free test |
| 3 | Generative AI with Large Language ModelsDeepLearning.AI & AWS / Coursera | Coursera plan3 | 2 weeks at 10 hours a week | Free test |
| 4 | Kaggle Learn Micro-Courses (ML, Python, SQL)Kaggle (Google) | Free4 | Short micro-courses, a few hours each | Free test |
Prove it free before you pay for an exam
Take the free test first. Pay for an exam only when you pass.
Every pick above has a matching free practice test on Science of Apps, one of the world's largest libraries of free certification tests (1,200+). Check your level before you pay for an exam, or add a verifiable credential to LinkedIn while you study. (Disclosure: we publish these tests; the certifications above earn us nothing.)
- Machine Learning Fundamentals CertificationPractise for Machine Learning Specialization before you pay.Start free test →
- Deep Learning CertificationPractise for Deep Learning Specialization before you pay.Start free test →
- LLM Application Development CertificationPractise for Generative AI with Large Language Models before you pay.Start free test →
Each option, and who should choose it
- CourseraMachine Learning SpecializationStart here$49/mo Coursera plan
- CourseraDeep Learning SpecializationNextCoursera plan
- CourseraGenerative AI with Large Language ModelsLLMsCoursera plan
Machine Learning Specialization
- Cost
- $49/mo Coursera plan1
- Time
- 2 months at 10 hours a week
About two months. Andrew Ng's specialization with Stanford teaches supervised and unsupervised learning, model evaluation and practical machine learning in Python. It takes about two months at ten hours a week and is widely regarded as the clearest structured start in ML.
Best forstart here
Deep Learning Specialization
- Cost
- Coursera plan2
- Time
- 3 months at 10 hours a week
About three months. The Deep Learning Specialization covers neural networks, optimisation, CNNs and sequence models in depth. It takes about three months at ten hours a week, assumes machine learning basics and can be audited free on Coursera.
Best fornext
Generative AI with Large Language Models
- Cost
- Coursera plan3
- Time
- 2 weeks at 10 hours a week
About two weeks. DeepLearning.AI and AWS's course explains how large language models work, how to fine-tune and evaluate them and how to deploy them. It takes about two weeks at ten hours a week and suits developers and data scientists with Python.
Best forLLMs
Kaggle Learn Micro-Courses (ML, Python, SQL)
- Cost
- Free4
- Time
- Short micro-courses, a few hours each
Free practice. Kaggle's free micro-courses cover Python, pandas, SQL, data visualisation and machine learning, each in a few hours with hands-on notebooks and a free certificate. They're ideal practice alongside a structured course.
Best forpractice
the ML Specialization vs the Deep Learning Specialization side by side
| the ML Specialization | the Deep Learning Specialization | |
|---|---|---|
| Level | Foundations | Advanced |
| Time | About 2 months | About 3 months |
| Prerequisite | Basic Python | ML foundations |
| Best for | ML beginners | Going deeper |
Table: the ML Specialization vs the Deep Learning Specialization
How to choose
- Choose the ML Specialization — you're new to machine learning.
- Choose the Deep Learning Specialization — you've finished ML foundations.
- Still unsure — search job posts in your city for each name.
How we ranked these picks
We compared both options on cost, format, prerequisites and fit using official pages, checked 3 November 2026. Science of Apps has no commercial relationship with either issuer.
Frequently asked questions
Can I skip the ML Specialization?
Only if you know ML basics.
Are they free?
Both can be audited free.
Which helps jobs?
Both, with projects.
What about LLMs?
Generative AI with LLMs after.
Is there a free practice test for both?
Yes. Science of Apps offers free practice tests for both, instantly graded with a verifiable certificate.
Sources
- DeepLearning.AI & Stanford — Machine Learning Specialization, official page (checked 3 Nov 2026)
- DeepLearning.AI — Deep Learning Specialization, official page (checked 3 Nov 2026)
- DeepLearning.AI & AWS — Generative AI with Large Language Models, official page (checked 3 Nov 2026)
- Kaggle (Google) — Kaggle Learn Micro-Courses (ML, Python, SQL), official page (checked 3 Nov 2026)
- Checked November 4, 2026. Figures that change — fees, salaries, prices — are reviewed every six months; if something is out of date, tell us.
Where to go from here
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