Professional certificate · Dedicated AI or machine learning program
Machine Learning Specialization
DeepLearning.AI · Online · provider based in United States
Currently offered, next intake unconfirmed · Hosted on Coursera
- Award
- Machine Learning Specialization
- Subject
- Machine learningSelf-paced, Flexible
- Format
- Fully online2 months at 10 hours a week
- Price
- Not published
Platform-hosted
About this program
Three-course online specialization covering supervised learning, neural networks, decision trees, unsupervised learning, recommender systems and reinforcement learning, created with Stanford Online.
- School and department
- DeepLearning.AI
- Subject or specialization
- Machine learningRecorded only where the official program page names it as a focus, not for individual modules.
- Teaching language
- English
Qualification and recognition
- Qualification
- Professional certificate
- Exact award
- Machine Learning Specialization
- Credential type
- Completion certificateA certificate of completion issued by the provider or platform. It is not a degree and not by itself an academic qualification.
- Academic credit
- Credit status not stated
- Awarding institution
- Not stated on the pages we checked
- Delivery platform
- Coursera
- Access and price model
- Access terms not statedThe official page does not state the price model. Page shows an Enroll for free button; terms of the free option are not stated, so no free model is recorded.
Structure and schedule
- Duration
- 2 months at 10 hours a week
- Delivery
- Fully onlineTaught entirely online. Check whether any in-person assessment is required before applying.
- Physical attendance
- Attendance requirement not verified
- Where students can enrol
- Not stated on the pages we checkedThe institution is based in United States; eligibility to enrol may differ.
- Live or recorded
- Live or asynchronous not confirmed
- Schedule
- Self-paced, FlexibleFlexible schedule, learn at your own pace
- Weekly commitment
- 10 hours a week
Source could not be re-checked automatically.
Admissions, prerequisites and intakes
- Programming
- Not stated
- Mathematics
- Not stated
- Prior degree
- Not stated
- Work experience
- Not stated
Stated audience: Beginners. Audience tags are recorded only where the official page says who the program is for. Missing entry requirements never mean “beginner friendly”.
- International applicants
- Not confirmed
Fees
Original currency is authoritative. Each fee keeps its basis (per term, per year, per credit, whole program) and the academic year it applies to and is never converted or added up for you.
Tuition is not published on the pages we checked. That does not mean the program is free: ask the institution for current fees, equipment costs and any deposit or refund terms.
Curriculum, assessment and projects
As published by the provider. None of this guarantees employment, admission or professional certification.
- Curriculum
- Supervised models with NumPy and scikit-learn, neural networks with TensorFlow, decision trees, unsupervised learning, recommenders and a deep reinforcement learning model.
Sources and verification
Not published on the program’s source pages: price, prerequisites, dated intake or deadline.
Sources and how this was checked
Last checked 1 November 2026. Each fact carries a verbatim quote that is re-checked against the live page. Research methodology.
Sources (checked 1 November 2026)