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
Machine Learning with Python course cover

₹0Free for good

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Overview

About 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.

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

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

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

    2 lessons · 28m
  5. 05

    Logistic regression and the confusion matrix

    2 lessons · 35m
  6. 06

    Decision trees, random forests and boosting

    4 lessons · 1h 7m
  7. 07

    SVM, KNN, Naive Bayes and scaling

    3 lessons · 53m
  8. 08

    Cross-validation, bias–variance and metrics

    3 lessons · 53m

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

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

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.

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

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.

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 EDWartens Certificate of Completion for Machine Learning with Python
Sample. The issued certificate carries your name, admission number, a unique certificate number 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 EDWartens India (Wartens Automation Private Limited) as a Certificate of Completion for this self-paced course. 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.

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?

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.

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, 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

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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.