AI and machine learning
Machine Learning with Python
Learn machine learning with scikit-learn on plant data, ending with a motor-fault classifier you train, tune and report.
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 takes about 18.1 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
- 18.1 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). scikit-learn, Pandas and matplotlib are normally available without installing; print the versions in your first cell. Hardware: none.
- Updated
After this course you can
By the end you will be able to:
- Frame a plant problem as regression, classification or clustering
- Split data without leakage, including by time and by machine
- Choose the right metric and read a confusion matrix
- Build pipelines, cross-validate and tune a model
- Save and serve a model as an API
Who it is for, and what you need first
Who it is for
Engineers, developers and students applying AI to real work.
Level: Beginner. Beginner in ML. Needs basic Python and Pandas — the Python for AI course, or equivalent.
What you need
Software: Google Colab (free). scikit-learn, Pandas and matplotlib are normally available without installing; print the versions in your first cell..
Hardware: None.. 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. 18.1 hours of video lessons, with written notes and a practice task in each module.
- 1What machine learning is, for an engineer4h 1m
- 2Linear regression: the line and the plane29m
- 3How a model learns: gradient descent, and saving models37m
- 4Categories, splitting and leakage, with lab practice: 28m
- 5Logistic regression and the confusion matrix, with lab practice: 35m
- 6Decision trees, random forests and boosting, with lab practice: 1h 7m
- 7SVM, KNN, Naive Bayes and scaling53m
- 8Cross-validation, bias–variance and metrics, with lab practice: 53m
Show the other 5 modules
- 9Feature engineering and outliers, with lab practice: 51m
- 10Pipelines, tuning and regularisation36m
- 11Unsupervised: k-means, PCA and anomalies49m
- 12The end-to-end project1h 3m
- 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.
scikit-learn, pandas and matplotlib are already installed in Colab.
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 habits behind every scikit-learn model: check the data, split without leakage, tune a tree against overfitting, and choose the metric and the threshold.
- Data health checkAI Workbench, about 20 minOpen in the lab (opens in a new tab): Data health check
- Train and evaluate without leakageAI Workbench, about 25 minOpen in the lab (opens in a new tab): Train and evaluate without leakage
- Overfitting: tune the treeAI Workbench, about 20 minOpen in the lab (opens in a new tab): Overfitting: tune the tree
- Choose the metric and the thresholdAI Workbench, about 25 minOpen in the lab (opens in a new tab): Choose the metric and the threshold
Optional. Skip it whenever you like: it never blocks your progress or your certificate.
Course project, about 7 hours
A motor-fault classifier from current, temperature and vibration features
Classify the fault of a motor from 2,400 readings across forty motors, with features you build from currents, temperatures and vibration. Split by motor, cross-validate, tune once and report the held-out confusion matrix with its limits.
- 9documents in the pack
- 8steps to follow
- Laboptional: in the 3D lab
- 6things to hand in
- 7 hof work, at your pace
- PlanProject plan: motor fault classifier from condition-monitoring readings
- URSRequirements and acceptance criteria for the motor fault classifier
- DatasheetDataset datasheet: motor-fleet-current-signatures.csv
- RegisterFeature register: derived features, formulas and physical reasons
- ProcedureNotebook procedure: from CSV to held-out confusion matrix
- RegisterExperiment log: every cross-validation run, including the ones that misled
Do it in the lab: 5 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: codebasics, Krish Naik, freeCodeCamp.org, StatQuest with Josh Starmer. Independent creators; the lessons stream from their own channels. EDWartens wrote the study plan, notes, practice tasks and assessments.
codebasics · Krish Naik · freeCodeCamp.org · StatQuest with Josh Starmer. 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
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?
There are two projects. The first is a motor-fault classifier on 2,400 readings from forty motors. It uses features, a split by motor, cross-validation, tuning and a held-out confusion matrix. The second is a daily kWh regression on two years of plant data. Its coefficients are ones the finance team can read.
Do I need a GPU?
No. Everything in this course runs on Colab's free CPU. It takes seconds, apart from the grid search in module 10, which can take a few minutes.
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.
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 Machine Learning with Python course cover, module by module?
The Machine Learning with Python course has 13 modules. It opens with "What machine learning is, for an engineer", goes through "Linear regression: the line and the plane", "Logistic regression and the confusion matrix", "Feature engineering and outliers" and "The end-to-end project" and ends with "Final assessment". The full list with minutes is under "What you learn" on this page.
What can I do after the Machine Learning with Python course?
After the Machine Learning with Python course: 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. 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 Machine Learning with Python course, and does it cost anything?
For the Machine Learning with Python course, software: Google Colab (free). scikit-learn, Pandas and matplotlib are normally available without installing; print the versions in your first cell. Hardware: none. The course itself is free.
Is the Machine Learning with Python course suitable for me, and what level is it?
Machine Learning with Python is rated beginner. Beginner in ML. Needs basic Python and Pandas — the Python for AI course, or equivalent. It is written for engineers, developers and students applying AI to real work, and takes about 18.1 hours at your own pace.
Which jobs use the skills from the Machine Learning with Python course?
Skills from the Machine Learning with Python course (for example: fit a regression whose coefficients you can explain in kWh per unit) 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
Machine Learning with Python, at your own pace. Add the certificate only if you want one.