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Machine learning foundations

Learn the statistics, workflow and scikit-learn practice behind reliable, explainable machine learning models.

No reviews yetTaught by Walnut Jobs course team
Level
Intermediate
Language
English
Duration
32 hours
Lessons
16

Last updated 22 Oct 2026

What you'll learn.

  • Use distributions, confidence intervals and hypothesis tests to reason about uncertainty.
  • Frame a business problem as a supervised learning task with a clear target and baseline.
  • Build leak-free scikit-learn pipelines with preprocessing and cross-validation.
  • Train and compare linear models, decision trees and gradient-boosted ensembles.
  • Choose evaluation metrics and thresholds that reflect the cost of errors.
  • Explain, document and monitor models in line with responsible AI practice.

How this course is taught.

Every module comes to you in four parts, so you can watch, read, discuss and check what you have learned.

  1. Quadrant I

    e-Tutorial

    Watch a short video lecture for each module, with full text notes beside it so you can review or translate at your own pace.

  2. Quadrant II

    e-Content

    Download or print the reading material for each module, with key terms, case studies and links to further resources.

  3. Quadrant III

    Discussion forum

    Ask questions and clear your doubts with other learners. The course team guides the discussion and answers questions.

  4. Quadrant IV

    Assessment

    Check your understanding with quizzes, short and long-answer practice and FAQs, then take the final test.

Curriculum.

5 modules · 16 lessons
1Statistics for machine learning3 lessons · 52 mine-Content (notes)2Assessment1
  • Note: Distributions, sampling and uncertainty20 min
  • Note: Hypothesis tests and experiments20 min
  • Quiz: Module 1 quiz12 min
2The machine learning workflow3 lessons · 48 mine-Content (notes)2Assessment1
  • Note: Framing a machine learning problem18 min
  • Note: Splitting data and avoiding leakage20 min
  • Quiz: Module 2 quiz10 min
3Supervised learning with scikit-learn4 lessons · 1 h 12 mine-Content (notes)3Assessment1
  • Note: Linear and logistic regression20 min
  • Note: Decision trees and gradient-boosted ensembles22 min
  • Note: Pipelines and preprocessing18 min
  • Quiz: Module 3 quiz12 min
4Evaluating and tuning models3 lessons · 52 mine-Content (notes)2Assessment1
  • Note: Classification metrics and thresholds22 min
  • Note: Cross-validation and hyperparameter tuning18 min
  • Quiz: Module 4 quiz12 min
5Responsible machine learning in production3 lessons · 1 he-Content (notes)2Final test1
  • Note: Fairness, explainability and documentation20 min
  • Note: Deploying and monitoring models20 min
  • Final test: Final assessment20 min

Your certificate.

This is the certificate you receive when you complete the course and pass its final test.

Certificate No.

WDT-WJMLF-2026-000000

Certificate of completion

Machine Learning Foundations

This is to certify that

Learner Name

has successfully completed the course and passed its assessment, and is recognised as a

Machine learning foundations

5 modules16 lessons32 hours of learningOnline, on Walnut LMS

DD Month YYYY

Date of issue

Noida, India

Authorised Signatory

Walnut DataTech Private Limited

Verify this certificate at walnutdatatech.com/verify-certificate with certificate number WDT-WJMLF-2026-000000. Issued on Walnut LMS.

Specimen — not a valid certificate
Machine learning foundationsView full size

What you need to receive it.

Complete every lesson and pass the final assessment with at least 70%.

Every certificate carries its own number that anyone can check on the verification page.

About this course.

Machine learning roles ask for more than library calls. Employers want people who can frame a problem, choose an honest evaluation, avoid leakage, explain a model to a risk committee and monitor it after release.

This course builds those foundations. You will start with the statistics that underpin every model, follow a disciplined workflow in scikit-learn, compare linear models with tree ensembles, choose metrics that match the business cost of errors and finish with fairness, explainability and monitoring.

Who this is for

  • Data analysts ready to move into data science
  • Software engineers who need to build or review ML features
  • Graduates in mathematics, economics or engineering preparing for ML interviews
  • Candidates whose target role lists machine learning, scikit-learn or statistics

Skills this certificate proves: Machine learning (Proficient), scikit-learn (Proficient), Statistics (Proficient).

Linked to Walnut Jobs: complete this course to earn the certificate employers ask for.

Your instructor.

Walnut Jobs course teamJob-ready courses designed with hiring companies

Learner reviews.

No reviews yet

No reviews yet. Learners can review a course after completing half of it.

Questions about enrolling.

Is this course really free?

Yes. Create an account and enrol with one click. There's no payment step.

How long can I access this course?

You keep access for as long as the course is offered on Walnut LMS.

Do I get a certificate?

Yes. The certificate section above shows the certificate and sets out what you need to receive it.

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