AI and machine learning · Free online course

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.

  • 13 modules
  • 12h 22m of video
  • Beginner
  • English
  • ₹0, free
  • Lab practice in your browser
Machine Learning with Python course cover

₹0Free for good

Worth ₹5,999 by length and depth against classroom training. It has always been free; that is not a former price.

No card. Checkout takes a minute and gives you a receipt.

Overview

About 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 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.

Who it is for: Engineers, developers and students applying AI to real work

This course includes

  • 12h 22m of video lessons
  • 13 modules with written notes and diagrams
  • One final assessment: 15 questions, pass mark 60%
  • Ask-an-engineer support from practising engineers
  • Lifetime access, the course does not expire
  • Optional verifiable certificate when you finish
  1. Step 1 of 3

    Learn the 13 modules

    12h 22m of video lessons, written notes and a practice task per module, at your own pace.

  2. Step 2 of 3

    Pass the final assessment

    15 questions drawn from every module, pass mark 60%, 3 attempts.

  3. Step 3 of 3

    Take the certificate, if you want it

    Optional, paid once, only if you choose it. The course stays free either way.

    See the certificate and its price

A look inside

Machine Learning with Python: Syllabus at a glance
Syllabus at a glance
Machine Learning with Python: What you will be able to do
What you will be able to do
Machine Learning with Python: Tools and credits
Tools and credits

From the lessons

  • Machine Learning Tutorial Python -1: What is Machine Learning?

    What machine learning is, for an engineer

    by codebasics

  • Machine Learning Tutorial Python - 4: Gradient Descent and Cost Function

    How a model learns: gradient descent, and saving models

    by codebasics

  • Machine Learning Tutorial Python - 8: Logistic Regression (Binary Classification)

    Logistic regression and the confusion matrix

    by codebasics

  • Machine Learning Tutorial Python - 10 Support Vector Machine (SVM)

    SVM, KNN, Naive Bayes and scaling

    by codebasics

  • What is feature engineering | Feature Engineering Tutorial Python # 1

    Feature engineering and outliers

    by codebasics

  • Machine Learning Tutorial Python - 13: K Means Clustering Algorithm

    Unsupervised: k-means, PCA and anomalies

    by codebasics

Lesson frames belong to the creators named in the Credits and are shown from YouTube.

What you learn

What you will be able to do

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

How it works

How this course works

  1. Watch the lessons

    Short video lessons, one module at a time, at your own pace.

  2. Read the notes

    Open the Reading tab for the written notes and diagrams of the module.

  3. Practise in the 3D labOptional

    Try the module in the free 3D lab, in your browser.

  4. Check yourself

    Answer the questions to see what stuck before you move on.

  5. Final assessment

    Pass the final once you have been through the modules.

  6. Certificate, if you want oneOptional

    Optional, with a page anyone can use to verify it.

Syllabus

Course content, module by module

In order, at whatever pace suits you. Each module ends with a practice task that builds on the last.

Modules
13
Video lessons
32
Of video
12h 22m
Final questions
15
  1. 01

    What machine learning is, for an engineer

    2 lessons · 4h 1m
  2. 02

    Linear regression: the line and the plane

    2 lessons · 29m
  3. 03

    How a model learns: gradient descent, and saving models

    2 lessons · 37m
  4. 04

    Categories, splitting and leakage, with lab practice:

    2 lessons · 28m
  5. 05

    Logistic regression and the confusion matrix, with lab practice:

    2 lessons · 35m
  6. 06

    Decision trees, random forests and boosting, with lab practice:

    4 lessons · 1h 7m
  7. 07

    SVM, KNN, Naive Bayes and scaling

    3 lessons · 53m
  8. 08

    Cross-validation, bias–variance and metrics, with lab practice:

    3 lessons · 53m

  9. Final assessment · 15 questions, pass mark 60%

3D lab

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.

Train and testWatch

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

Project

The course project

Optional practice that ends in the kind of job the course prepares you for, with the documents that go with it.

About 7 hours

A motor-fault classifier from current, temperature and vibration features

You have 2,400 readings from a fleet of forty motors. They cover phase currents, winding and ambient temperature, three vibration bands and THD. Each is labelled healthy, bearing, rotor bar or imbalance. Engineer the features, split by motor, cross-validate and tune. Then report the held-out confusion matrix like an engineer would.

This project at a glance
  1. 9documents in the pack
  2. 7steps to follow
  3. Laboptional: in the 3D lab
  4. 6things to hand in
  5. 7 hof work, at your pace

Do it in the lab: 5 steps.

Sample document pack, 9 documents, filled in for the scenario

  • 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
  • Change noticeChange notice: four grouped folds dealt by fault (URS clause 2.2)
  • Test reportAcceptance test record: motor fault classifier, held-out motors
  • ReportEvaluation report: can one CMS reading name a motor's fault?

Read inside the course and download as a workbook. The project is optional practice, marked when you submit it; the certificate needs only the modules and the final assessment.

About 5 hours

Predict daily plant kWh from production, weather and run hours

You have 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, with coefficients that carry units. Check the residuals. Then beat it with a forest and say whether the gain is worth the lost readability.

This project at a glance
  1. 8documents in the pack
  2. 6steps to follow
  3. Laboptional: in the 3D lab
  4. 6things to hand in
  5. 5 hof work, at your pace

Do it in the lab: 5 steps.

Sample document pack, 8 documents, filled in for the scenario

  • PlanProject plan: day-ahead electricity forecast for the Blumenau plant
  • URSRequirements for the daily kWh forecast
  • DatasheetDataset datasheet: plant-daily-energy.csv
  • RegisterMeter-fault register: days excluded and why
  • ProcedureNotebook procedure: cleaning, split, models and residuals
  • Test reportAcceptance test record: daily kWh forecast on the six test months
  • ReportForecast report: what a day of production costs in electricity, and how well we can predict it
  • O&M manualOperating instructions: the daily forecast at the energy desk

Read inside the course and download as a workbook. The project is optional practice, marked when you submit it; the certificate needs only the modules and the final assessment.

Tools

Software, hardware and lessons

Software

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

Hardware

None.

Who it is for

Beginner in ML. Needs basic Python and Pandas — the Python for AI course, or equivalent.

Video lessons by

  • codebasics
  • Krish Naik
  • freeCodeCamp.org
  • StatQuest with Josh Starmer

Independent creators, credited in full under Credits.

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

  1. 01

    Google Colab

    Google, in the browser

    Free, GPU time not guaranteed
    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.

    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.

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.

Sample Wartens Certificate of Completion for Machine Learning with Python, issued through the EDWartens training portal
Sample. The issued certificate carries your name, your final score, a unique certificate ID and its own QR code.

  • 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 Wartens Ltd (England and Wales, company no. 15262249) through the EDWartens training portal, as a Certificate of Completion for this self-paced course. Sold in India by Wartens Automation Private Limited. 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.

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.

See the 3D lab
  • 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.

  • 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.

FAQ

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 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.

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.

Is the Machine Learning with Python course free in India, and what does the certificate cost?

Yes. Learning costs ₹0 in India: every module, the written notes, the practice tasks and the final assessment, with no card and no trial period. The only paid item is the optional EDWartens Certificate of Completion, ₹499 including GST for this beginner course, paid in rupees through Razorpay, and only if you want it after passing the final assessment.

All course facts
Price
₹0, free for good. No trial, no card. Comparable classroom training of this length costs about ₹5,999.
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, ₹499 inclusive of GST for learners in India, 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

Pairs well with

Siemens TIA Portal free course coverPLC programming · FreeSiemens TIA PortalSiemens TIA Portal in Three Hours free course coverPLC programming · FreeSiemens TIA Portal in Three HoursTIA Portal: Build a Machine free course coverPLC programming · FreeTIA Portal: Build a MachineInstrumentation for PLC Engineers free course coverInstrumentation · FreeInstrumentation for PLC Engineers

More free courses: Free AI courses for engineers

Learning paths with this course

Classroom course, Bangalore

PLC training in Bangalore

The Automation Engineer Program (AEP) is our classroom course in PLC programming and industrial automation, taught in person at our Electronic City centre.

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.