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intermediateCertificate$99

Principles of Machine Learning

by Microsoft Team · edX

4.4
(2,500 reviews)
70K+ enrolled6 weeksUpdated 2024-05
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Our Verdict

Consider alternatives

Skip this listing in 2026: Microsoft's "Principles of Machine Learning" (edX course code DAT203.2x) was part of the Microsoft Professional Program, which Microsoft retired, and the official edX page our catalog links to now returns a 404 with no live enrollment. When it was active it earned only a modest 4.09 out of 5 from 11 aggregated reviews (Class Central / freeCodeCamp's David Venturi roundup), and its most consistent criticism was that the labs hand you mostly pre-written R/Python code inside Azure Machine Learning Studio rather than making you write models yourself. The six-module syllabus (classification, regression, model improvement, tree/ensemble methods, neural networks and SVMs, clustering and recommenders) was genuinely well-scoped for an intermediate learner and only needed about 18-24 hours, but it is tightly coupled to the now-legacy classic Azure ML Studio interface. Because the course is effectively unavailable and its tooling is dated, learners should choose a maintained alternative instead.

The official edX page (microsoft-principles-of-machine-learning) returns HTTP 404, the course was part of Microsoft's retired Professional Program in Data Science, and Microsoft's own GitHub lab repositories for it are also gone (404). Even setting availability aside, it relied on the legacy classic Azure ML Studio drag-and-drop tool and held only a 4.09/5 rating, with reviewers noting almost no hands-on coding was required. There is no reason to pursue this specific listing when maintained, higher-rated alternatives exist.

Best for: Historically it suited intermediate learners who already knew basic statistics and some R or Python and wanted a fast, breadth-first tour of supervised and unsupervised ML algorithms with minimal math, using Azure ML Studio to see models work end-to-end. In 2026, realistically it is only of archival interest to people who already enrolled and still have course access, or who specifically want to revisit the classic Azure ML Studio workflow.

Skip if: Anyone trying to enroll today (the page is dead and the certificate path via the Microsoft Professional Program no longer exists), learners who want to actually write model code from scratch rather than run pre-filled notebooks/experiments, those wanting current tooling (modern Azure ML, scikit-learn, or PyTorch instead of the deprecated classic Azure ML Studio), and complete beginners who need foundations to be taught slowly.

About This Course

Microsoft course covering classification, regression, feature engineering, model selection, and ensemble methods with Azure ML.

What You'll Learn

Build and evaluate classification models and interpret metrics like accuracy, precision, recall and ROC/AUC
Build regression models and assess them with error metrics such as RMSE and R-squared
Apply techniques to improve models: regularization, cross-validation, hyperparameter tuning and feature engineering/selection
Use decision trees and ensemble methods (e.g. random forests, boosting) to raise predictive performance
Understand optimization-based methods including neural networks and support vector machines (SVMs)
Apply unsupervised methods: clustering and building recommender systems
Construct end-to-end ML experiments in Azure Machine Learning while using R and Python for the analytical steps

Curriculum

Classification

Introduction to classification and building classification models; evaluating classifiers.

Regression

Introduction to regression and creating/evaluating regression models.

Improving Machine Learning Models

Principles and techniques for model improvement: regularization, cross-validation, hyperparameter tuning and feature selection.

Tree and Ensemble Methods

Decision trees and ensemble methods such as bagging/random forests and boosting.

Optimization-Based Methods

Neural networks and support vector machines (SVMs).

Clustering and Recommenders

Unsupervised clustering and building recommender systems.

Prerequisites

  • Basic statistics and probability (the course assumes you understand concepts like distributions and correlation rather than teaching them)
  • Working knowledge of either Python (NumPy/Pandas) or R, since the hands-on labs use both alongside Azure ML
  • Recommended completion of the earlier Microsoft Professional Program data-science courses (it was the 7th in a 10-course series), e.g. introductory data science and Python/R fundamentals

Instructor

Microsoft Team

Instructor · edX

Pros & Cons

Pros

  • Broad, well-organized syllabus covering both supervised (classification, regression, trees/ensembles, neural nets, SVMs) and unsupervised (clustering, recommenders) ML in a logical progression
  • Low time commitment of roughly 18-24 hours total (about 3-4 hours/week over 6 weeks), making it a quick conceptual overview
  • Could be audited for free, with the paid verified certificate optional rather than required to access the material
  • Azure ML Studio's visual experiment interface let learners see complete ML pipelines run end-to-end without heavy setup

Cons

  • The official edX course page is now a 404 and the course was retired along with the Microsoft Professional Program, so it generally cannot be enrolled in or completed today
  • Reviewers repeatedly noted the labs provide mostly pre-written code with little original coding expected, which limits how much practical skill you build
  • Built around the legacy classic Azure ML Studio drag-and-drop tool, which is deprecated and not how modern ML work (or current Azure ML) is done
  • Modest 4.09/5 aggregate rating from a small sample (11 reviews), with feedback that concepts were not always explained in depth

Alternatives To Consider

Machine Learning Specialization

Coursera

View course

Intro to Machine Learning

Kaggle

View course

Azure AI Fundamentals

Microsoft Learn

View course

Frequently Asked Questions

Is Principles of Machine Learning free?

Principles of Machine Learning is $99. Was free to audit on edX; verified certificate previously cost roughly the listed ~$99 (catalog states $99). In 2026 this is moot because the course page returns 404 and the certificate path via the retired Microsoft Professional Program no longer exists.

Who is Principles of Machine Learning for?

Historically it suited intermediate learners who already knew basic statistics and some R or Python and wanted a fast, breadth-first tour of supervised and unsupervised ML algorithms with minimal math, using Azure ML Studio to see models work end-to-end. In 2026, realistically it is only of archival interest to people who already enrolled and still have course access, or who specifically want to revisit the classic Azure ML Studio workflow.

What will you learn in Principles of Machine Learning?

Build and evaluate classification models and interpret metrics like accuracy, precision, recall and ROC/AUC; Build regression models and assess them with error metrics such as RMSE and R-squared; Apply techniques to improve models: regularization, cross-validation, hyperparameter tuning and feature engineering/selection; Use decision trees and ensemble methods (e.g. random forests, boosting) to raise predictive performance.

What are the prerequisites for Principles of Machine Learning?

Basic statistics and probability (the course assumes you understand concepts like distributions and correlation rather than teaching them); Working knowledge of either Python (NumPy/Pandas) or R, since the hands-on labs use both alongside Azure ML; Recommended completion of the earlier Microsoft Professional Program data-science courses (it was the 7th in a 10-course series), e.g. introductory data science and Python/R fundamentals.

Is Principles of Machine Learning worth it?

The official edX page (microsoft-principles-of-machine-learning) returns HTTP 404, the course was part of Microsoft's retired Professional Program in Data Science, and Microsoft's own GitHub lab repositories for it are also gone (404). Even setting availability aside, it relied on the legacy classic Azure ML Studio drag-and-drop tool and held only a 4.09/5 rating, with reviewers noting almost no hands-on coding was required. There is no reason to pursue this specific listing when maintained, higher-rated alternatives exist.

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on edX's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

Sources

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