On 2026-10-08 the Deep Learning Specialization on Coursera shows 'Enroll for free' with a session starting Oct 8 and financial aid available; deeplearning.ai lists 107 short courses and a Pro membership with Free and Pro tiers. source
Andrew Ng's Coursera specializations (Deep Learning, and Machine Learning with Stanford) plus a catalog of short GenAI courses on DeepLearning.AI's own site, paid by subscription at roughly $49 to $59 a month on Coursera or $300 a year for DeepLearning.AI Pro, with free audit and free short-course videos. It suits people who want structured ML fundamentals and a recognised certificate at low cost, not anyone expecting placement help. Before paying, confirm whether you need the certificate at all, since the videos can be watched free and the Coursera time estimates tend to overstate the work.
The facts
| Category | AI and machine learning |
|---|---|
| Website | deeplearning.ai |
| Based | Palo Alto, CA · founded 2017 |
| Format | online-self-paced |
| Programs | Deep Learning Specialization (5 courses, Coursera), Machine Learning Specialization (3 courses, with Stanford Online, Coursera), Mathematics for Machine Learning and Data Science, Data Engineering and Data Analytics specializations, 107 short courses on learn.deeplearning.ai (GenAI apps, prompt engineering, agents, RAG, LLMOps) |
| Length | 13 weeks (Deep Learning Specialization: 5 courses, about 129 hours, Coursera's estimate '3 months at 10 hours a week'. Machine Learning Specialization: 3 courses, '2 months at 10 hours a week'. Short courses run about 1-2 hours each.) verified |
| Tuition | $0 to $399 Subscription pricing, not tuition. Coursera: Machine Learning Specialization page lists $49/month after a 7-day free trial, audit is free without certificate; Coursera Plus is $59/month or $399/year (40% off monthly) with a 14-day refund on annual. DeepLearning.AI's own platform: Free tier ($0, all course videos) and Pro at $50/month monthly or $25/month billed annually ($300/year) for labs and certificates. The Deep Learning Specialization page itself did not display a price while logged out. verified |
| Financing | upfront, free, scholarship Coursera: 'Enroll for free', 7-day free trial on the subscription, financial aid available on application (Coursera specialization pages). Audit access to course videos is free; graded assignments and certificates require payment. DeepLearning.AI Pro: $50/mo or $300/yr. No job guarantee anywhere on the sites. |
| Admission | open No application; Coursera enrollment only. ML Specialization is beginner level; DL Specialization is intermediate and assumes Python and basic linear algebra. |
| Stack | Python, NumPy, TensorFlow, Keras, neural networks, CNNs, RNNs/LSTMs, Transformers, decision trees, recommender systems, reinforcement learning, prompt engineering, RAG, agents |
Outcomes
This program does not publish job-placement or salary outcomes. Treat any number you hear from admissions as unverifiable.
Interview-prep depth
0 / 5
Public syllabi for both specializations are ML fundamentals coursework with graded labs; no interview, DS&A, system design or behavioral modules.
Whatever the program covers, the interview itself is free to prepare for here: coding patterns, system design, practice prompts, company guides.
What a careful reader should know
Founded in 2017 by Andrew Ng (deeplearning.ai/about); California registry entries show Deeplearning.ai LLC formed May 1, 2017 in Palo Alto. Coursera ratings as of 2026-10-08: Deep Learning Specialization 4.8 from 147,266 reviews with 1,003,951 enrolled; Machine Learning Specialization (with Stanford Online) 4.9 from 39,386 reviews with 847,833 enrolled. Instructors: Andrew Ng, Younes Bensouda Mourri and Kian Katanforoosh (DL); Ng, Eddy Shyu, Aarti Bagul and Geoff Ladwig (ML). Short courses are hosted on DeepLearning.AI's own platform and partner with OpenAI, Anthropic, LangChain, Google Cloud, Hugging Face and Meta; videos are free on the Free tier and labs/certificates need Pro. The company reports no job outcomes and offers no job guarantee. Reddit commenters treat the two specializations as the standard starting point and note Coursera's weekly-hours estimates run high relative to actual material.
Third-party signals: Reddit sentiment positive. Graduation-time star ratings are collected by the schools; weigh them accordingly.
Sources (7)
This directory takes no lead fees or placement payments from any program. Where a program runs an affiliate scheme we say so on its page and mark the link. Facts carry a badge: verified means we read it on a primary source on the date shown, reported means a third party said it, unknown means nobody we trust has published it.
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