AI and machine learning · Free · ₹0
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
Course content
13 modules · 31 lessons · 12h 6m
In order, at whatever pace suits you. Each module ends with a practice task that builds on the last, and a short quiz.
- 01What machine learning is, for an engineer3 lessons4h 6m
- 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 bagging3 lessons51m
- 07SVM, KNN, Naive Bayes and scaling3 lessons53m
- 08Cross-validation, bias–variance and metrics3 lessons53m
- 09Feature engineering and outliers3 lessons45m
- 10Pipelines, tuning and regularisation2 lessons36m
- 11Unsupervised: k-means, PCA and anomalies2 lessons49m
- 12The end-to-end project4 lessons1h 3m
- 13Final assessment0 lessons0m
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.
- Hardware
- None.
Machine Learning with Python at a glance
Machine Learning with Python is a free, self-paced online ai and machine learning course from EDWartens India with 13 modules, 12h 6m of video lessons, written notes, practice tasks and assessments, and an optional verifiable certificate.
- Price
- ₹0, free for good. No trial, no card. Comparable classroom training of this length costs about ₹5,999.
- Format
- 13 self-paced modules, 12h 6m of video, written notes, a quiz per module and a 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 (independent creators, credited below)
- Language
- English
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.
- Add it to your LinkedIn profile in one click
- Link it from a CV or portfolio, the URL is permanent
- Publicly verifiable by code, not a PDF anyone can edit
- Issued by EDWartens India, the training centre itself
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.
- Questions answered in the context of the module you are on
- The same engineers who teach the AEP programme
- Career tools, CV help and public job listings included
- Your progress and notes stay in your account for good
Pairs well with
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PLC programming · FreeSiemens S7-1200 with TIA Portal: The Tutorial SeriesEvery S7-1200 instruction, one short video at a time: bits, edges, timers, counters, comparators, maths, MOVE, conversion, analog scaling, jumps and shift registers.
HMI · FreeSiemens TIA Portal HMI: KTP and Comfort Panels with WinCCEvery screen a plant operator touches, built in TIA Portal: tags, buttons, symbols, numeric input, bar graphs, navigation and templates, trends, alarms, recipes, faceplates, pop-ups, users and languages.
Process control · FreeSiemens TIA Portal PID Compact and Analog ProcessingClosed-loop control the way it is done on an S7-1200 or S7-1500: analog in, NORM_X and SCALE_X, PID_Compact configured, tuned and wired to an HMI, analog or PWM out, proved on a free tank simulation.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?
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 submitted a project and passed the final assessment, 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.
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
A unique certificate number and a public verification page. A recruiter checks it in ten seconds.
Adds to LinkedIn in one click
Issuer, credential ID and URL prefilled, with 5 matching skills to pin to your profile.
QR code on the certificate
Scans straight to the verification page, so a printed copy proves itself.
Names what you can do
Lists the topics covered, from supervised and unsupervised learning with scikit-learn to a motor-fault classifier trained, tuned and reported.
A permanent link
Put it on a CV, a portfolio or an application. The URL never changes.
Earned, not attended
Issued only after every module and a final assessment at 70%, with three attempts. That is why it stands up.
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, ₹459, GST included, 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.
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
If you are one of these creators and would like a lesson removed or credited differently, write to info@wartens.com.