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EDWartens UK

Free course · AI and machine learning

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.

See the modules

Free to learn. Verifiable certificate £19 when you pass the final exam (optional, VAT included).

Level
Beginner
Modules
13
Video
18 hours
Price
Free

Outcome

What you will be able to do

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

Software

Google Colab (free). scikit-learn, Pandas and matplotlib are pre-installed.

Hardware

None.

Syllabus

13 modules

Each module has video lessons, notes and a short quiz. The final exam closes the course.

  1. 1What machine learning is, for an engineer2 lessons
  2. 2Linear regression: the line and the plane2 lessons
  3. 3How a model learns: gradient descent, and saving models2 lessons
  4. 4Categories, splitting and leakage2 lessonsLab
  5. 5Logistic regression and the confusion matrix2 lessonsLab
  6. 6Decision trees, random forests and boosting4 lessonsLab
  7. 7SVM, KNN, Naive Bayes and scaling3 lessons
  8. 8Cross-validation, bias–variance and metrics3 lessonsLab
  9. 9Feature engineering and outliers4 lessonsLab
  10. 10Pipelines, tuning and regularisation2 lessons
  11. 11Unsupervised: k-means, PCA and anomalies2 lessons
  12. 12The end-to-end project4 lessons
  13. 13Final assessment0 lessons

How it works

How this course works

  1. Watch the video lessonsOne module at a time, at your own pace.
  2. Take the module quizA short quiz at the end of each module shows what stuck.
  3. Keep your notesWrite in My notes beside each module and ask the trainer if you are stuck.
  4. Practise in the labOptionalTry a task from the module in the EDWartens Lab, in your browser.
  5. Sit the final examPass it once you have been through the modules.
  6. Certificate, if you want oneOptionalPaid, and only after you pass. Anyone can check it online.

EDWartens Lab

Practise this in the EDWartens Lab

Train a model on plant data, move the decision threshold and weigh the cost of errors in the Lab's AI workbench.

Works alongside Machine Learning with Python. The Lab is ai for industry practice in your browser: no install, no hardware, free to use. Sign in as a learner and it opens with your account.

Open the free lab See the Lab
AI for industry: Build AI models.

Levels to try with this course

Optional. Skip it whenever you like: it never blocks your progress or your certificate.

Why practise in a 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.

Certificate

An optional certificate when you pass

Learning is free. Pass the final exam and you can have a certificate for £19, including VAT, with a number employers can check online. It records that you completed a free online course; it is not a regulated qualification or a CPD certificate.

  • Your name, the course, its study time and your final exam score
  • A unique certificate number and a QR code to its verification page
  • Issued by Wartens Ltd; add it to your LinkedIn profile in one click
  • Pay now or after you pass: it is issued the moment you pass the final

How the certificate works

Sample certificate for Machine Learning with Python

Credits

Who made the lessons

The video lessons belong to their creators. EDWartens wrote the study plan, notes, quizzes and the final exam.

  • codebasics

    the Machine Learning Tutorial Python series, the feature-engineering lessons and the end-to-end project

  • Krish Naik

    the R-squared and bias–variance explanations

  • freeCodeCamp.org

    Machine Learning for Everybody, the companion course

  • StatQuest with Josh Starmer

    a lesson in "Decision trees, random forests and boosting"

Questions

About this course

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?
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 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 do I get?
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?
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.

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The Professional Module is five live days of Siemens PLC, HMI and SCADA with an instructor, then a resume session and a CPD certificate.

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