AI and machine learning · Free
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.
Inside the course
From the lessons
Colab, variables, numbers and strings
TensorFlow
Conditions, loops and comprehensions
Corey Schafer
Files: CSV and JSON from loggers and gateways
Corey Schafer
Classes: objects, methods and attributes
Corey Schafer
Pandas: DataFrames from real files
codebasics
Time series: resample, rolling, shift and calendars
codebasics
Lesson frames belong to the creators named in the Credits below and are shown from YouTube.
What you will learn
Write Python confidently in Google Colab; use lists, dicts, loops, functions and classes; read and write CSV and JSON; handle errors in real files; do vectorised maths with NumPy; load, clean, resample, group and plot engineering data with Pandas and matplotlib; and deliver a reproducible notebook that turns a messy meter export into a load report.
- Run Python in Google Colab and read an error message without panic
- Convert 4–20 mA readings, scale signals and check NAMUR limits in code
- Turn a vendor I/O list into a clean JSON tag map
- Read, clean and de-duplicate a data-logger CSV with Pandas
- Resample 15-minute meter data to daily kWh and monthly maximum demand
- Plot a week of load with limits and a hour-by-day heatmap, and explain it
For you
Taking Python for AI and Engineering Data from the United States
- Free in the United States, as everywhere, and self-paced: lessons, notes and the final assessment are open at any hour, so your time zone and shift pattern do not matter.
- The optional certificate for learners in the United States is a one-off US$23.99. What you get for it
- Plants across the Americas most often run Allen-Bradley, Siemens and Inductive Automation; each has its own free course to take next.
- See automation and engineering jobs in the United States, and what the industry looks like in Houston, Detroit and Chicago.
- EDWartens also has a regional site for the United States, for classroom training and local support: edwartens.com/us.
The course project · 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.
The course project · 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 — inconsistent tag names, mixed units, blank cells — and write a small Python tool that cleans it, checks it and writes a JSON tag map a SCADA or Node-RED import can use.
Course content
13 modules · 44 lessons · 12h 22m
In order, at whatever pace suits you. Each module ends with a practice task that builds on the last.
- 01Colab, variables, numbers and strings3 lessons36m
- 02Lists, tuples, sets and dictionaries5 lessons1h 34m
- 03Conditions, loops and comprehensions4 lessons52m
- 04Functions and the standard library3 lessons1h 12m
- 05Files: CSV and JSON from loggers and gateways3 lessons1h 1m
- 06Errors, exceptions and debugging2 lessons25m
- 07Classes: objects, methods and attributes3 lessons47m
- 08NumPy: arrays, masks and signals4 lessons1h 35m
Requirements
- Who it is for
- Absolute beginner. No programming experience assumed; any electrical or automation background helps you see the point of every example.
- Software
- Google Colab (free, in the browser). Nothing to install. What to download, and how
- Hardware
- None. Any laptop or even a phone browser for the lessons; a laptop for the project.
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.
Python for AI and Engineering Data at a glance
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.
All course factsHide course facts
- Price
- Free, for good. No trial, no card. The only paid item is the optional certificate, a small one-off fee.
- 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, 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
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.
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.
Pairs well with
PLC programming · FreeSiemens TIA PortalFrom zero electrical knowledge to a working, simulated S7-1200 program, for nothing.
PLC programming · FreeSiemens TIA Portal in Three HoursThe first three hours of the Siemens TIA Portal course, cut to end on a win: what a PLC is, how it is wired, a project configured in TIA Portal, and your first ladder program running in simulation. Finish it in an evening or two, earn a certificate, and carry straight on into the full course.
PLC programming · FreeTIA Portal: Build a MachineOne machine, start to finish. Take a bottle filling line from a written specification and an I/O list to a structured S7-1200 program with a fill station, a capper, a reject sorter and an operator screen with alarms, then test it against a written record and archive it for hand-over. The lessons are the reference; the machine is yours, and it is what you submit.
Instrumentation · FreeInstrumentation for PLC EngineersThe half of the loop that is not code. Follow one measurement from the transmitter in the field, down the 4-20 mA loop, into the analog card, through NORM_X and SCALE_X into engineering units, out again to a valve, and back to the control room when the reading is wrong.More free courses: Free AI courses for engineers
Learning paths with this course
- AI foundations for engineers · 4 coursesFrom zero code to a working AI assistant: Python on engineering data, how large language models actually work, prompting for engineering documents, and AI agents and workflow automation with n8n.
Learner reviews
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Reviews here are written only by learners who have finished every module of Python for AI and Engineering Data, and they are published exactly as written. Finish the course and yours will be the first.
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.
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 EDWartens, the training division of Wartens, as a Certificate of Completion for this self-paced course. Sold by Wartens Ltd (England and Wales). 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.
- 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.