Predictive Maintenance with Machine Learning

No. The course uses the track's vibration windows and motor-fleet readings, and NASA's public engine dataset. The module on sensors tells you what to buy and where to mount it when you do.

The Machine Learning with Python course, or equivalent: features, splits, cross-validation, metrics. Module 7 uses the Deep Learning course's tools but is optional.

There are two projects. The first is remaining useful life on NASA's C-MAPSS FD001 engines, evaluated the way the benchmark is. The second covers the two lower rungs on the track's own data. It has an anomaly detector with persistence on vibration windows. It also has a fault classifier with cost-aware thresholds on the motor fleet, with alert text a technician could act on.

Yes. Every module, practice task, project and assessment. You create an account so your progress is saved and the assessments can be marked. The certificate is the only paid item, and only if you want it.

An EDWartens Certificate of Completion, issued when you have finished the modules and passed the final assessment (the project is optional practice), with a verification code anyone can check. It is not a vendor credential and is never described as one.

The creators named in the Credits block at the foot of this page, on their own YouTube channels. EDWartens did not make the videos and the creators are not affiliated with EDWartens. What EDWartens wrote is the study plan, the notes, the practice tasks, the projects and the assessments.

Plan on about 16 hours in all, including the video lessons, notes and practice tasks. It is self-paced, so you work through the twelve modules at your own speed.

No. The lab is optional practice. You can skip it whenever you like, and it never blocks your progress, the final assessment or your certificate.

Yes. Every lab level for this course opens in your browser, free, with nothing to install.

The Predictive Maintenance with Machine Learning course has 12 modules. It opens with "Why predictive, and the P-F curve", goes through "Failure modes and what each sensor sees", "Anomaly detection on normal data", "Remaining useful life on NASA's engines" and "Deploying and measuring" and ends with "Final assessment". The full list with minutes is under "What you learn" on this page.

After the Predictive Maintenance with Machine Learning course: Choose the sensor by the warning time you need, and name the failure mode from its spectrum; Turn windows into condition indicators — kurtosis, band energies, envelope peaks — and rank them; Build a per-asset anomaly detector on healthy data with a threshold and persistence that stop alert floods. These are the practical outcomes the course is written around, and the final assessment of 15 questions checks them.

For the Predictive Maintenance with Machine Learning course, software: Google Colab; NumPy, SciPy, Pandas, scikit-learn; TensorFlow for the optional deep-learning module. Hardware: none. Wireless vibration sensors and a gateway are discussed, not required. The course itself is free.

Predictive Maintenance with Machine Learning is rated intermediate. Intermediate. Needs the Machine Learning with Python course; the Deep Learning course helps for module 7. It is written for engineers, developers and students applying AI to real work, and takes about 15.7 hours at your own pace.

Skills from the Predictive Maintenance with Machine Learning course (for example: choose the sensor by the warning time you need, and name the failure mode from its spectrum) are used in roles such as AI engineer, machine learning engineer, data analyst and automation developer, and in machine learning engineer work. The course gives you practical evidence of the skill; it does not promise a job, and employers decide on experience and interview.