AI and machine learning · Free
Machine Learning with Python
scikit-learn end to end on plant data: regression on energy against production, classification of motor-current signatures, the honest evaluation habits that make a model trustworthy, and a motor-fault classifier trained, tuned and reported the way an interviewer wants to see it.
Inside the course
From the lessons
What machine learning is, for an engineer
codebasics
How a model learns: gradient descent, and saving models
codebasics
Logistic regression and the confusion matrix
codebasics
SVM, KNN, Naive Bayes and scaling
codebasics
Feature engineering and outliers
codebasics
Unsupervised: k-means, PCA and anomalies
codebasics
Lesson frames belong to the creators named in the Credits below and are shown from YouTube.
What you will learn
Frame a plant problem as regression, classification or clustering; fit and read linear and logistic regression; use trees, random forests, SVM, KNN and Naive Bayes; split without leakage, including by time and by machine; choose the right metric and read a confusion matrix; engineer physics-based features; build pipelines, cross-validate and tune with GridSearchCV; use k-means, PCA and IsolationForest; save and serve a model as an API.
- Fit a regression whose coefficients you can explain in kWh per unit
- Split train and test without leakage — by time for logs, by motor for fleets
- Read a confusion matrix and choose recall or precision with the person who owns the cost
- Engineer features from physics: imbalance, temperature rise, vibration ratios, rolling trends
- Cross-validate, tune with GridSearchCV and report a score with its spread
- Train a motor-fault classifier, save it with joblib and serve it as an API
For you
Taking Machine Learning with Python 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 6 hours
A motor-fault classifier from current, temperature and vibration features
2,400 readings from a fleet of forty motors — phase currents, winding and ambient temperature, three vibration bands, THD — labelled healthy, bearing, rotor bar or imbalance. Engineer the features, split by motor, cross-validate, tune, and report the held-out confusion matrix like an engineer would.
The course project · about 4 hours
Predict daily plant kWh from production, weather and run hours
Two years of daily energy for a textile plant with production units, ambient temperature, run hours and the dominant process. Fit a regression you can explain — coefficients with units — check the residuals, then beat it with a forest and say whether the gain is worth the lost readability.
Course content
13 modules · 32 lessons · 12h 22m
In order, at whatever pace suits you. Each module ends with a practice task that builds on the last.
- 01What machine learning is, for an engineer2 lessons4h 1m
- 02Linear regression: the line and the plane2 lessons29m
- 03How a model learns: gradient descent, and saving models2 lessons37m
- 04Categories, splitting and leakage2 lessons28m
- 05Logistic regression and the confusion matrix2 lessons35m
- 06Decision trees, random forests and boosting4 lessons1h 7m
- 07SVM, KNN, Naive Bayes and scaling3 lessons53m
- 08Cross-validation, bias–variance and metrics3 lessons53m
Requirements
- Who it is for
- Beginner in ML. Needs basic Python and Pandas — the Python for AI course, or equivalent.
- Software
- Google Colab (free). scikit-learn, Pandas and matplotlib are pre-installed. What to download, and how
- Hardware
- None.
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.
scikit-learn, pandas and matplotlib are already installed in Colab.
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.
Machine Learning with Python at a glance
Machine Learning with Python 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 codebasics, Krish Naik, 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. Beginner in ML. Needs basic Python and Pandas — the Python for AI course, or equivalent.
- Brand
- Vendor-neutral
- Software
- Google Colab (free). scikit-learn, Pandas and matplotlib are pre-installed.
- Hardware
- None.
- Certificate
- Optional EDWartens Certificate of Completion, verifiable by code. Not a vendor credential.
- Video lessons by
- codebasics, Krish Naik, freeCodeCamp.org, StatQuest with Josh Starmer (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
- Applied AI engineer · 4 coursesThe four skills AI job posts name most: machine learning with scikit-learn, computer vision with OpenCV, deep learning with TensorFlow and Keras, and RAG chatbots with LangChain — each with a plant project.
Learner reviews
No reviews yet
Reviews here are written only by learners who have finished every module of Machine Learning with Python, and they are published exactly as written. Finish the course and yours will be the first.
Common questions
How much maths do I need?
Arithmetic and the willingness to read a formula. Gradient descent is shown by hand once so you see it; after that scikit-learn does the calculus and you do the engineering.
Do I need to know Python first?
Yes, at the level of the Python for AI and Engineering Data course: DataFrames, functions, plotting. Do that course first if you have not coded.
What are the projects in the Machine Learning with Python course?
A motor-fault classifier on 2,400 readings from forty motors — features, a split by motor, cross-validation, tuning and a held-out confusion matrix — and a daily kWh regression on two years of plant data with coefficients the finance team can read.
Do I need a GPU?
No. Everything in this course runs on Colab's free CPU in seconds.
Is the Machine Learning with Python 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 Machine Learning with Python 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 Machine Learning with Python 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 projects and the assessments.
What you walk away with
Your certificate for Machine Learning with Python
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.
- codebasicsthe Machine Learning Tutorial Python series, the feature-engineering lessons and the end-to-end project
- Krish Naikthe R-squared and bias–variance explanations
- freeCodeCamp.orgMachine Learning for Everybody, the companion course
- StatQuest with Josh Starmera lesson in "Decision trees, random forests and boosting"
If you are one of these creators and would like a lesson removed or credited differently, write to info@wartens.com.