AI and machine learning · Free · ₹0
Predictive Maintenance with Machine Learning
The industrial AI application with a budget behind it, built end to end: failure modes and what each sensor sees, signals to condition indicators, anomaly detection on normal data, fault classification, deep learning on windows, remaining useful life on NASA's run-to-failure engines, and the alerting and deployment that decide whether anyone acts.
Inside the course
From the lessons
Why predictive, and the P-F curve
RealPars
Failure modes and what each sensor sees
ADASH
Signals: sampling, windows, spectra
MATLAB
Condition indicators
MATLAB
Anomaly detection on normal data
Dave Ebbelaar
Fault classification
Intelligent Machines
Lesson frames belong to the creators named in the Credits below and are shown from YouTube.
What you will learn
Choose sensors by the P-F curve; recognise bearing, imbalance, misalignment, rotor-bar and insulation signatures; compute spectra, envelopes and condition indicators from windows; build a per-asset anomaly detector with a percentile threshold and persistence; train a fault classifier with grouped validation and cost-aware thresholds; estimate remaining useful life on the C-MAPSS benchmark; design alerts that get acted on; and deploy and measure the whole loop.
- 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
- Train a fault classifier with grouped validation and thresholds set by cost
- Estimate remaining useful life on the NASA C-MAPSS benchmark with the right target and metrics
- Design alerts a technician acts on, deploy the loop on an edge box, and measure the programme
Course content
12 modules · 27 lessons · 10h 3m
In order, at whatever pace suits you. Each module ends with a practice task that builds on the last, and a short quiz.
- 01Why predictive, and the P-F curve3 lessons23m
- 02Failure modes and what each sensor sees2 lessons11m
- 03Signals: sampling, windows, spectra1 lesson43m
- 04Condition indicators2 lessons22m
- 05Anomaly detection on normal data3 lessons1h 54m
- 06Fault classification3 lessons28m
- 07Deep learning on windows4 lessons42m
- 08Remaining useful life on NASA's engines3 lessons55m
- 09The end-to-end workflow3 lessons2h 36m
- 10Alerts that get acted on1 lesson51m
- 11Deploying and measuring2 lessons59m
- 12Final assessment0 lessons0m
Requirements
- Who it is for
- Intermediate. Needs the Machine Learning with Python course; the Deep Learning course helps for module 7.
- 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.
Predictive Maintenance with Machine Learning at a glance
Predictive Maintenance with Machine Learning is a free, self-paced online ai and machine learning course from EDWartens India with 12 modules, 10h 3m of video lessons, written notes, practice tasks and assessments, and an optional verifiable certificate.
- Price
- ₹0, free for good. No trial, no card. Comparable classroom training of this length costs about ₹5,999.
- Format
- 12 self-paced modules, 10h 3m of video, written notes, a quiz per module and a final assessment.
- Level
- Intermediate. Intermediate. Needs the Machine Learning with Python course; the Deep Learning course helps for module 7.
- Brand
- Vendor-neutral
- 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.
- Certificate
- Optional EDWartens Certificate of Completion, verifiable by code. Not a vendor credential.
- Video lessons by
- MATLAB, RealPars, ADASH, Intelligent Machines, Dave Ebbelaar, Data Science with Marco, NeuralNine, Krish Naik, Victor Tan, Data Bowl Recipes, JCharisTech (independent creators, credited below)
- Language
- English
A shareable EDWartens certificate
Finish every module and pass the final assessment, and the optional EDWartens certificate is yours. It carries a unique verification code on a public page anyone can check, so it stands up when a recruiter looks it up. See it below.
- Add it to your LinkedIn profile in one click
- Link it from a CV or portfolio, the URL is permanent
- Publicly verifiable by code, not a PDF anyone can edit
- Issued by EDWartens India, the training centre itself
The course itself stays free whether or not you ever buy one.
Stuck? Ask a practising engineer
A free course usually means a comment section and hope. This one does not. Every module has an Ask-your-trainer panel that reaches the same engineers who teach our paid programme: people who commission panels for a living, not moderators.
- Questions answered in the context of the module you are on
- The same engineers who teach the AEP programme
- Career tools, CV help and public job listings included
- Your progress and notes stay in your account for good
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- Industrial AI engineer · 4 coursesAI where the machines are: where it sits beside PLC and SCADA, predictive maintenance on real run-to-failure data, machine vision quality inspection with YOLO, and plant data over Modbus and OPC UA in Python.
Learner reviews
No reviews yet
Reviews here are written only by learners who have finished every module of Predictive Maintenance with Machine Learning, and they are published exactly as written. Finish the course and yours will be the first.
Common questions
Do I need vibration sensors?
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.
What do I need before this course?
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.
What are the projects?
Remaining useful life on NASA's C-MAPSS FD001 engines, evaluated the way the benchmark is — and the two lower rungs on the track's own data: an anomaly detector with persistence on vibration windows and a fault classifier with cost-aware thresholds on the motor fleet, with alert text a technician could act on.
Is the course really free?
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.
What certificate do I get?
An EDWartens Certificate of Completion, issued when you have submitted a project and passed the final assessment, with a verification code anyone can check. It is not a vendor credential and is never described as one.
Who made the video lessons?
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.
What you walk away with
Your certificate for Predictive Maintenance with Machine Learning
Finish the course, pass the final, and this is the document with your name on it.
Verifiable by anyone
A unique certificate number and a public verification page. A recruiter checks it in ten seconds.
Adds to LinkedIn in one click
Issuer, credential ID and URL prefilled, with 5 matching skills to pin to your profile.
QR code on the certificate
Scans straight to the verification page, so a printed copy proves itself.
Names what you can do
Lists the topics covered, from failure modes, condition monitoring and the p-f curve to a predictive maintenance model built and reported.
A permanent link
Put it on a CV, a portfolio or an application. The URL never changes.
Earned, not attended
Issued only after every module and a final assessment at 70%, with three attempts. That is why it stands up.
Learning is free. The certificate is optional.
Add it now and pay only when you have finished the course, or come back for it later. One-off, ₹559, GST included, with a receipt.
Issued by EDWartens India (Wartens Automation Private Limited) as a Certificate of Completion for this self-paced course. It is not a vendor certification, a university award or a CPD-accredited activity, and it does not certify competence on live equipment. Delivered electronically; see the refund policy.
Credits
Who made the video lessons
The video lessons in this course were created by the people below, not by EDWartens. Every lesson streams from its creator's own YouTube channel; EDWartens neither hosts nor sells that footage, and the creators are not affiliated with EDWartens and do not endorse this course. What EDWartens wrote is the study plan, the notes, the practice tasks and the assessments.
- MATLABthe predictive maintenance series, the signal-processing talk and the prognostics case study
- RealParspredictive maintenance explained and machine learning for predictive maintenance
- ADASHvibration analysis for beginners
- Intelligent Machinesmachine learning and deep learning for fault diagnosis, and the turbofan RUL videos
- Dave Ebbelaardetecting outliers in sensor data
- Data Science with Marcoanomaly detection in time series
- NeuralNineanomaly detection for time-series data
- Krish Naikthe complete anomaly detection tutorial
- Victor Tanthe end-to-end predictive maintenance workflow
- Data Bowl Recipespredictive maintenance with machine learning
- JCharisTechpredictive maintenance with machine learning in Python
If you are one of these creators and would like a lesson removed or credited differently, write to info@wartens.com.