AI and machine learning · Free online course
Python for AI and Engineering Data
Python from zero, taught on the data an engineer actually has: tag lists, 4–20 mA readings, meter logs and motor currents. Variables to Pandas time series in twelve modules, all in a free Google Colab notebook, ending with a real year of plant energy data cleaned and reported.
- 13 modules
- 12h 22m of video
- Beginner
- English
- ₹0, free
- Lab practice in your browser
₹0Free for good
Worth ₹5,999 by length and depth against classroom training. It has always been free; that is not a former price.
No card. Checkout takes a minute and gives you a receipt.
Overview
About Python for AI and Engineering Data
Learn Python from zero on engineering data: tag lists, meter logs and motor currents, in a free Colab notebook.
Python for AI and Engineering Data is a free, self-paced online course from EDWartens for engineers, developers and students applying AI to real work. It has 13 modules and 12h 22m of video lessons by Corey Schafer, codebasics, freeCodeCamp.org and others, with written notes and worked problems, a practical project with a document pack and a 15-question final assessment (pass mark 60%). Learning is free with an account; an optional certificate with a public verification code is issued when you pass. Last updated 27 September 2026.
Who it is for: Engineers, developers and students applying AI to real work
This course includes
- 12h 22m of video lessons
- 13 modules with written notes and diagrams
- One final assessment: 15 questions, pass mark 60%
- Ask-an-engineer support from practising engineers
- Lifetime access, the course does not expire
- Optional verifiable certificate when you finish
- Step 1 of 3
Learn the 13 modules
12h 22m of video lessons, written notes and a practice task per module, at your own pace.
- Step 2 of 3
Pass the final assessment
15 questions drawn from every module, pass mark 60%, 3 attempts.
- Step 3 of 3
Take the certificate, if you want it
Optional, paid once, only if you choose it. The course stays free either way.
See the certificate and its price
A look inside
From the lessons
Colab, variables, numbers and strings
by TensorFlow
Conditions, loops and comprehensions
by Corey Schafer
Files: CSV and JSON from loggers and gateways
by Corey Schafer
Classes: objects, methods and attributes
by Corey Schafer
Pandas: DataFrames from real files
by codebasics
Time series: resample, rolling, shift and calendars
by codebasics
Lesson frames belong to the creators named in the Credits and are shown from YouTube.
What you learn
What you will be able to do
By the end you will be able to:
- Write Python in Google Colab
- Use lists, dicts, loops, functions and classes
- Read and write CSV and JSON files
- Clean, resample, group and plot data with Pandas
- Deliver a reproducible notebook that turns a meter export into a load report
How it works
How this course works
Watch the lessons
Short video lessons, one module at a time, at your own pace.
Read the notes
Open the Reading tab for the written notes and diagrams of the module.
Practise in the 3D labOptional
Try the module in the free 3D lab, in your browser.
Check yourself
Answer the questions to see what stuck before you move on.
Final assessment
Pass the final once you have been through the modules.
Certificate, if you want oneOptional
Optional, with a page anyone can use to verify it.
Syllabus
Course content, module by module
In order, at whatever pace suits you. Each module ends with a practice task that builds on the last.
- Modules
- 13
- Video lessons
- 44
- Of video
- 12h 22m
- Final questions
- 15
- 01
Colab, variables, numbers and strings
3 lessons · 36m - 02
Lists, tuples, sets and dictionaries
5 lessons · 1h 34m - 03
Conditions, loops and comprehensions
4 lessons · 52m - 04
Functions and the standard library
3 lessons · 1h 12m - 05
Files: CSV and JSON from loggers and gateways
3 lessons · 1h 1m - 06
Errors, exceptions and debugging
2 lessons · 25m - 07
Classes: objects, methods and attributes
3 lessons · 47m - 08
NumPy: arrays, masks and signals
4 lessons · 1h 35m Final assessment · 15 questions, pass mark 60%
3D lab
Practise in the 3D lab
Practise the checks you code in Pandas by eye first: find blanks, copied rows and wild values in a table before anyone trains or reports on it.
- Data health checkAI Workbench, about 20 minOpen in the lab (opens in a new tab): Data health check
Optional. Skip it whenever you like: it never blocks your progress or your certificate.
Project
The course project
Optional practice that ends in the kind of job the course prepares you for, with the documents that go with it.
About 4 hours
Clean a year of compressor-house energy data and write the load report
A real-shaped 15-minute energy-meter export with gaps, duplicates, offline readings and scaling spikes. Clean it in Pandas and produce the daily kWh table, the maximum-demand figure and the hour-by-day heatmap a plant manager would act on.
- 8documents in the pack
- 8steps to follow
- Laboptional: in the 3D lab
- 6things to hand in
- 4 hof work, at your pace
Do it in the lab: 4 steps.
Sample document pack, 8 documents, filled in for the scenario
- PlanData Analysis Plan: Compressor-House Energy Log 2025-26
- DatasheetData Source Sheet: Compressor-House Incomer Meter and Log File
- RegisterData-Quality Issue Register
- ProcedureCleaning and Analysis Procedure (Notebook Method)
- Test reportVerification Record: Cleaned Series and Computed Figures
- ReportCompressor-House Load Report, 01-Sep-2025 to 31-Aug-2026
- Change noticeChange Notice: Spike Rule and Gap Fill Method
- Handover certificateHand-over of the Cleaned Dataset and Load Report
Read inside the course and download as a workbook. The project is optional practice, marked when you submit it; the certificate needs only the modules and the final assessment.
About 3 hours
Turn a vendor's messy I/O list into a clean tag map
Take a panel builder's I/O list exactly as it comes, with inconsistent tag names, mixed units and blank cells. Write a small Python tool that cleans it and checks it. The tool writes a JSON tag map that a SCADA or Node-RED import can use.
- 7documents in the pack
- 6steps to follow
- Laboptional: in the 3D lab
- 6things to hand in
- 3 hof work, at your pace
Do it in the lab: 4 steps.
Sample document pack, 7 documents, filled in for the scenario
- URSRequirements: I/O List to SCADA Tag-Map Converter
- RegisterI/O List Extract as Received, Section 1 (Test Fixture)
- RegisterPlanted-Fault Key
- SDSSoftware Design: tagmap.py
- Test reportTest Record: tagmap.py against the Fixture
- ReportProblems Report to the Panel Builder, Section 1
- RegisterClean Tag Map, Section 1 (tagmap.json as a Table)
Read inside the course and download as a workbook. The project is optional practice, marked when you submit it; the certificate needs only the modules and the final assessment.
Tools
Software, hardware and lessons
Software
Google Colab (free, in the browser). Nothing to install.
Hardware
None. Any laptop or even a phone browser for the lessons; a laptop for the project.
Who it is for
Absolute beginner. No programming experience assumed; any electrical or automation background helps you see the point of every example.
Video lessons by
- Corey Schafer
- codebasics
- freeCodeCamp.org
- TensorFlow
Independent creators, credited in full under Credits.
Software you need
What to download, where from, what it costs and how to install it. Every link goes to the maker's own site, never a mirror.
Nothing to install: everything runs in Google Colab in the browser.
Required
- 01Free, GPU time not guaranteed
Google Colab
Google, in the browser
- Runs on
- Any modern web browser
- Account
- A free Google account
Colab is free to use. In the free version GPUs and TPUs are heavily restricted and not guaranteed, sessions can run for at most 12 hours, and idle sessions are stopped. Paid plans give more reliable access.
Steps
- 1.Open colab.research.google.com and sign in with your Google account.
- 2.Click New notebook (or open the notebook your course links to).
- 3.For a GPU, choose Runtime, then Change runtime type, then pick a GPU if one is offered.
- 4.Type code in a cell and press Shift and Enter to run it.
- Save a copy to your Google Drive so your changes are kept.
- Files on the runtime are deleted when the session ends. Save outputs to Drive.
Open Google Colabcolab.research.google.com
Checked against each maker's own page on 27 September 2026. Trial lengths and editions change; the maker's page is the final word.
What you walk away with
Your certificate for Python for AI and Engineering Data
Finish the course, pass the final, and this is the document with your name on it.
Verifiable by anyone
Adds to LinkedIn in one click
QR code on the certificate
Names what you can do
A permanent link
Earned, not attended
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, US$23.99, with a receipt.
Issued by Wartens Ltd (England and Wales, company no. 15262249) through the EDWartens training portal, as a Certificate of Completion for this self-paced course. Sold in India by Wartens Automation Private Limited. 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.
Practise what you learn in the free 3D lab
Technical work is judged on what you can do: wiring, commissioning, finding a fault. Doing a task yourself teaches more than watching it a second time, and a lab lets you try it safely before you meet real equipment. It does not replace working on real hardware.
- Practise safely, so a mistake costs nothing.
- The lab checks your work like a site test, so you know when it is right.
- Free, in your browser, with nothing to install.
Why learn with EDWartens
What you get with this course.
- Free from the first lesson to the final assessment.
- Practice in a free 3D lab in your browser, with nothing to install.
- Video lessons with written notes and a project.
- An optional certificate that anyone can verify online.
FAQ
Common questions
I have never programmed. Is this the right place to start?
Yes. It assumes nothing and every example is engineering data, so you always know why a line of code exists. Finish it before Machine Learning with Python.
Do I need to install Python?
No. Everything runs in Google Colab in the browser, which is free and has NumPy, Pandas and matplotlib ready. A Gmail account is all you need.
What is the project in the Python for AI and Engineering Data course?
A year of 15-minute compressor-house energy data, with the gaps, duplicates, offline blocks and spikes a real export has. You clean it, compute daily kWh, maximum demand and shift averages, plot it and write the report. It is marked against a rubric within about a minute of submitting.
Is the Python for AI and Engineering Data 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 does the Python for AI and Engineering Data course give?
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.
Who made the video lessons in the Python for AI and Engineering Data course?
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 project and the assessments.
How long does the Python for AI and Engineering Data course take?
Plan on about 18 hours in all, including the video lessons, notes and practice tasks. It is self-paced, so you work through the thirteen modules at your own speed.
Do I have to use the lab?
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.
Is the lab free?
Yes. Every lab level for this course opens in your browser, free, with nothing to install.
Is the Python for AI and Engineering Data course free in India, and what does the certificate cost?
Yes. Learning costs ₹0 in India: every module, the written notes, the practice tasks and the final assessment, with no card and no trial period. The only paid item is the optional EDWartens Certificate of Completion, ₹499 including GST for this beginner course, paid in rupees through Razorpay, and only if you want it after passing the final assessment.
All course factsHide course facts
- Price
- ₹0, free for good. No trial, no card. Comparable classroom training of this length costs about ₹5,999.
- Who it is for
- Engineers, developers and students applying AI to real work
- Format
- 13 self-paced modules, 12h 22m of video, written notes, a practice task per module and one final assessment.
- Level
- Beginner. Absolute beginner. No programming experience assumed; any electrical or automation background helps you see the point of every example.
- Brand
- Vendor-neutral
- Software
- Google Colab (free, in the browser). Nothing to install.
- Hardware
- None. Any laptop or even a phone browser for the lessons; a laptop for the project.
- Certificate
- Optional EDWartens Certificate of Completion, ₹499 inclusive of GST for learners in India, verifiable by code. Not a vendor credential.
- Video lessons by
- Corey Schafer, codebasics, freeCodeCamp.org, TensorFlow (independent creators, credited below)
- Language
- English
- Last updated
- 27 September 2026
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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.
- Corey Schaferthe Python tutorials for beginners and the matplotlib series
- codebasicsthe Pandas and NumPy tutorials
- freeCodeCamp.orgthe NumPy course for beginners
- TensorFlowa lesson in "Colab, variables, numbers and strings"
If you are one of these creators and would like a lesson removed or credited differently, write to info@wartens.com.