If you are choosing between Andrew Ng is two flagship courses — the Machine Learning Specialization and the Deep Learning Specialization — here is how to decide. Same teacher, same DeepLearning.AI brand, very different prerequisites and outcomes.
Side-by-side comparison
| Dimension | ML Specialization | DL Specialization |
|---|---|---|
| Length | 3 courses, ~3 months part-time | 5 courses, ~4 months part-time |
| Prerequisites | High-school math, some Python | ML Spec or equivalent, comfortable Python |
| What you build | Linear/logistic regression, neural nets basics, recommenders | CNNs, RNNs, transformers, sequence-to-sequence |
| Math intensity | Moderate | Higher — comfort with linear algebra needed |
| Job market signal | Data Scientist, ML Engineer (entry) | Applied AI roles, computer vision, NLP |
| Cost | Free to audit · $49/mo for cert | Free to audit · $49/mo for cert |
Take the ML Specialization if…
- You are new to ML and want the canonical foundations
- Your target role is “data scientist” or “ML engineer” at most companies
- You want to work on classical ML problems (forecasting, recommendations, churn)
- You want a single 3-month commitment that gets you employable
Take the Deep Learning Specialization if…
- You have finished the ML Spec (or have equivalent professional ML experience)
- Your target work involves images, audio, video, or natural language
- You want to understand transformer-based models (the foundation of LLMs)
- You are aiming at research-adjacent or AI-focused engineering roles
What if you only have time for one?
The Machine Learning Specialization. It produces a more employable graduate for the median ML role in 2026. Add the Deep Learning Specialization later when your career direction clarifies.
What about the Generative AI with LLMs course?
If your interest is specifically in LLMs, RAG, fine-tuning, and prompt engineering — skip both and start with the Generative AI with Large Language Models course. It is shorter (3 weeks), assumes ML literacy, and is closer to what modern AI engineers actually do day-to-day.
A small editorial collective of working data professionals, software engineers, career coaches, and former hiring managers. We have collectively taken 200+ of the courses listed on this site. We do not use AI to generate course reviews or rankings.
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