AI and machine learning
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 takes about 18.3 hours, with written notes, a practice task per module, a practical project and a final assessment of 15 questions (pass mark 60%). Learning costs nothing and needs only a free account. The optional EDWartens Certificate of Completion, verifiable by code, is a one-off US$23.99 for you; it is not a vendor credential. Updated 1 November 2026.
- Free
- Beginner
- 18.3 hours
- Self-paced
- Certificate optional
- Lab practice in your browser
No card, no trial. The course is free for good. Certificate US$23.99, optional.
- Who it is for
- Engineers, developers and students applying AI to real work
- Jobs
- Roles that use this skill: AI engineer, machine learning engineer, data analyst and automation developer. No course guarantees a job.
- Tools
- 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.
- Updated
After this course you can
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
Who it is for, and what you need first
Who it is for
Engineers, developers and students applying AI to real work.
Level: Beginner. Absolute beginner. No programming experience assumed; any electrical or automation background helps you see the point of every example.
What you need
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.. What to download, and how.
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.
What you learn
13 modules in order, at your own pace. 18.3 hours of video lessons, with written notes and a practice task in each module.
- 1Colab, variables, numbers and strings36m
- 2Lists, tuples, sets and dictionaries1h 3m
- 3Conditions, loops and comprehensions52m
- 4Functions and the standard library1h 12m
- 5Files: CSV and JSON from loggers and gateways1h 1m
- 6Errors, exceptions and debugging25m
- 7Classes: objects, methods and attributes47m
- 8NumPy: arrays, masks and signals1h 35m
Show the other 5 modules
- 9Pandas: DataFrames from real files, with lab practice: 1h 5m
- 10Cleaning and grouping engineering data, with lab practice: 46m
- 11Time series: resample, rolling, shift and calendars54m
- 12Plotting and the reproducible report1h 30m
- 13Final assessment
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.
Show what to download (1 tool)
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.
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.
Course project, about 4 hours
Clean a year of compressor-house energy data and write the load report
Clean a year of 15-minute energy-meter data that has copied rows, an offline block, missing intervals and scaling spikes. Report the daily kWh, the maximum demand and the shift averages, with two charts a plant manager can act on.
- 8documents in the pack
- 9steps to follow
- Laboptional: in the 3D lab
- 7things to hand in
- 4 hof work, at your pace
- 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
Do it in the lab: 4 steps.
The project is optional practice. The certificate needs only the modules and the final assessment.
After the course, your next step
Show what you can do, then keep going in the same field.
A certificate you can verify
Finish the modules and pass the final assessment (15 questions, pass mark 60%) and you can add a certificate with a public verification page. One-off, US$23.99 for you. It is not a vendor credential.
Want a free certificate? Do the matching Free internship: 4 weeks, nothing to buy, with a free certificate of completion.
AI and Machine Learning
Eight short weeks of machine learning from data to a deployed model: notes, diagrams, a few videos and worked exercises in free notebooks. Finish weeks 1 to 4 and the final, then claim a free Internship Completion Certificate with your real dates and study hours, and a QR code anyone can scan to verify it. Training, not employment.
See the internshipPart of a learning path
Courses in a planned order, so each one builds on the last.
More free courses
Roles that use this skill
- AI engineer
- Machine learning engineer
- Data analyst
- Automation developer
These are the kinds of roles where the skill is used. We make no promise of a job or an interview.
Plain notes
- Free: the whole course, the notes, the practice tasks, the final assessment and support from practising engineers.
- Paid, and optional: the course certificate, and the Engineer-verified internship (the Free internship costs nothing).
- No job promise: the course teaches a skill. It does not place you in a job.
- Lessons by: Corey Schafer, codebasics, freeCodeCamp.org, TensorFlow. Independent creators; the lessons stream from their own channels. EDWartens wrote the study plan, notes, practice tasks and assessments.
Corey Schafer · codebasics · freeCodeCamp.org · TensorFlow. If you are one of these creators and want a lesson removed or credited differently, write to info@edwartens.com.
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, and what comes after it.
- 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.
- Free internships to keep practising afterwards.
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 Google 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.
What topics does the Python for AI and Engineering Data course cover, module by module?
The Python for AI and Engineering Data course has 13 modules. It opens with "Colab, variables, numbers and strings", goes through "Lists, tuples, sets and dictionaries", "Files: CSV and JSON from loggers and gateways", "Pandas: DataFrames from real files" and "Plotting and the reproducible report" and ends with "Final assessment". The full list with minutes is under "What you learn" on this page.
What can I do after the Python for AI and Engineering Data course?
After the Python for AI and Engineering Data course: 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. These are the practical outcomes the course is written around, and the final assessment of 15 questions checks them.
What software and hardware do I need for the Python for AI and Engineering Data course, and does it cost anything?
For the Python for AI and Engineering Data course, 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. The course itself is free.
Is the Python for AI and Engineering Data course suitable for me, and what level is it?
Python for AI and Engineering Data is rated beginner. Absolute beginner. No programming experience assumed; any electrical or automation background helps you see the point of every example. It is written for engineers, developers and students applying AI to real work, and takes about 18.3 hours at your own pace.
Which jobs use the skills from the Python for AI and Engineering Data course?
Skills from the Python for AI and Engineering Data course (for example: run Python in Google Colab and read an error message without panic) 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.
Start free today
Python for AI and Engineering Data, at your own pace. Add the certificate only if you want one.