DeepLearning.AI · Professional Certificate · Associate
Machine Learning Specialization (Stanford / DeepLearning.AI)
Crydd Score 79
Demand Growing
Career Value Strong
Recognition High
ML Specialization at a glance
- Provider
- DeepLearning.AI
- Credential type
- Professional Certificate
- Exam required
- No — course/project based
- Category
- AI & Machine Learning
- Level
- Associate
- Cost
- Coursera subscription; audit free, certificate paid; financial aid available
- Difficulty
- Intermediate
- Time to earn
- 2–4 months
- Validity
- Does not expire
- Format
- Graded assignments and labs across three courses
- Prerequisites
- Basic Python and high-school maths
What is ML Specialization?
Andrew Ng's foundational machine-learning course, rebuilt with DeepLearning.AI and Stanford. It is the single most widely-taken introduction to ML, and the reference point most practitioners share.
Skills it covers
- Supervised learning
- Neural networks
- Model evaluation
- Python
- scikit-learn
Is ML Specialization worth it? The Crydd Verdict
CS 79Choose it if
You want the canonical, rigorous introduction to machine learning from the person who taught most of the field.
Consider alternatives if
You want a completely free option — CS50 AI or Kaggle's ML courses cover related ground at no cost.
Why we say so
Unmatched recognition and teaching quality make it the standard ML foundation, and Coursera's financial aid keeps it accessible.
Market demand
It is the most commonly cited machine-learning course in the industry, and completing it is a recognised signal of serious intent.
Where ML Specialization takes you
- Machine Learning Engineer
- Data Scientist
- AI Engineer
Career roadmaps show where this certification fits in each path. See the roadmaps.
Related certifications
Curated connections from the catalog — each labelled with how it relates to ML Specialization, so you know why it’s here.
