Machine Learning with Python
by Saeed Aghabozorgi · Coursera
Our Verdict
Worth it — with caveatsMachine Learning with Python is a solidly-rated (4.7/5 from 18,300+ Coursera ratings), hands-on introduction to applied ML that teaches you to build and evaluate models with Python and scikit-learn rather than derive the math behind them. As of its 2024 refresh it is co-taught by Joseph Santarcangelo and Saeed Aghabozorgi and serves as Course 1 of IBM's AI Engineering Professional Certificate, so it is best understood as a practitioner on-ramp, not a theory course. The current version is labeled intermediate (the catalog's 'beginner' tag is outdated) and now spans roughly 20 hours across regression, supervised models (decision trees, SVM, KNN, Random Forest, XGBoost), unsupervised methods (K-Means, DBSCAN/HDBSCAN, PCA/t-SNE/UMAP), rigorous evaluation (cross-validation, GridSearchCV, data-leakage), and a final rainfall-prediction project plus exam. Learners consistently praise the code-along labs and clear step-by-step structure, but recurring criticism is that the material is condensed, light on mathematical depth, and occasionally hampered by grading/auto-grader friction. Take it to get productive with scikit-learn fast; pair it with a theory-heavy course (e.g. Andrew Ng) if you want to understand why the algorithms work.
Excellent, well-rated practical scikit-learn primer with strong labs and a real capstone, but it deliberately skips mathematical derivations and moves fast, so it is the right pick only if you want applied skills (and ideally are pursuing the IBM AI Engineering certificate) rather than first-principles ML theory.
Best for: Beginners-to-intermediate learners who already know basic Python and want to become productive with scikit-learn quickly through guided, hands-on labs; people working toward the IBM AI Engineering Professional Certificate (this is Course 1); and practitioners who prefer learning by building real models (fraud detection, customer segmentation, rainfall prediction) over reading proofs.
Skip if: Learners who want deep mathematical/statistical foundations or to implement algorithms from scratch (gradient descent, backprop, the linear-algebra behind PCA); complete programming novices with zero Python; and anyone seeking deep-learning/neural-network coverage, which this course does not include.
About This Course
IBM course covering regression, classification, clustering, and recommender systems using scikit-learn and SciPy.
What You'll Learn
Curriculum
Foundational ML concepts, where Python and scikit-learn fit, the model lifecycle, ML engineer roles, open-source tooling, and a first model built with scikit-learn (~2 hours).
Simple and multiple linear regression, polynomial and non-linear regression, and logistic regression for classification, with train/test workflow and hands-on labs (~3 hours).
Classification methods, decision and regression trees, Support Vector Machines (fraud detection), K-Nearest Neighbors, ensemble models (Random Forest, XGBoost), and bias-variance tradeoffs (~4 hours).
Clustering with K-Means, DBSCAN and HDBSCAN, dimensionality reduction via PCA, t-SNE and UMAP, and real-world customer segmentation (~3 hours).
Classification and regression metrics, confusion matrices, cross-validation, regularization, ML pipelines, GridSearchCV hyperparameter tuning, and identifying data leakage (~4 hours).
Practice project on the Titanic dataset, a final rainfall-prediction classifier project, and a comprehensive final exam (~4 hours).
Prerequisites
- Basic Python programming (the course applies Python rather than teaching it from zero)
- Comfort with Jupyter Notebooks (labs run in-browser via Coursera/SkillsNetwork labs)
- Helpful but not strictly required: familiarity with basic statistics and high-school-level linear algebra to follow the model intuition
Instructor
Saeed Aghabozorgi
Instructor · Coursera
Pros & Cons
Pros
- Strongly rated and battle-tested: 4.7/5 from 18,300+ Coursera ratings with 680K+ enrollments, so the quality signal is large and consistent
- Genuinely hands-on - code-along Jupyter labs and real applications (fraud detection, customer segmentation, rainfall prediction) let you 'go straight to practice' as reviewers note
- Up-to-date 2024 curriculum that adds modern, job-relevant tools (XGBoost, HDBSCAN, UMAP, GridSearchCV) and explicitly teaches data-leakage avoidance and proper validation
- Clear, step-by-step structure that learners credit for explaining what to do and why, and it can be audited for free without paying
- Counts toward the IBM AI Engineering Professional Certificate and yields a shareable IBM/Coursera certificate
Cons
- Light on mathematical depth - it focuses on calling scikit-learn APIs rather than deriving algorithms, so you won't deeply understand the underlying math
- Several learners describe the content as condensed/fast-paced, wanting more time and explanation per section
- Recurring complaints about course logistics and auto-grading friction on assignments
- Some find IBM's instructional tone 'academic but bland'; no deep-learning/neural-network coverage despite being an 'AI Engineering' prerequisite
Alternatives To Consider
Frequently Asked Questions
Is Machine Learning with Python free?
Machine Learning with Python is $49/mo. Free to audit the lecture content; graded assignments and the shareable certificate require the paid Certificate experience (Coursera ~$49/mo subscription or a free trial; financial aid available). Included with Coursera Plus.
Who is Machine Learning with Python for?
Beginners-to-intermediate learners who already know basic Python and want to become productive with scikit-learn quickly through guided, hands-on labs; people working toward the IBM AI Engineering Professional Certificate (this is Course 1); and practitioners who prefer learning by building real models (fraud detection, customer segmentation, rainfall prediction) over reading proofs.
What will you learn in Machine Learning with Python?
Build and interpret regression models: simple/multiple linear, polynomial, non-linear, and logistic regression with scikit-learn; Apply core supervised classifiers - decision trees, regression trees, K-Nearest Neighbors, and Support Vector Machines (incl. an SVM fraud-detection lab); Use ensemble methods including Random Forest and XGBoost, and reason about the bias-variance tradeoff; Perform unsupervised learning: K-Means, DBSCAN and HDBSCAN clustering plus customer-segmentation applications.
What are the prerequisites for Machine Learning with Python?
Basic Python programming (the course applies Python rather than teaching it from zero); Comfort with Jupyter Notebooks (labs run in-browser via Coursera/SkillsNetwork labs); Helpful but not strictly required: familiarity with basic statistics and high-school-level linear algebra to follow the model intuition.
Is Machine Learning with Python worth it?
Excellent, well-rated practical scikit-learn primer with strong labs and a real capstone, but it deliberately skips mathematical derivations and moves fast, so it is the right pick only if you want applied skills (and ideally are pursuing the IBM AI Engineering certificate) rather than first-principles ML theory.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Coursera'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
- Official Coursera course page - Machine Learning with Python (IBM): syllabus, 4.7 rating, 18,400 reviews, 688,103 enrolled, intermediate level
- Reddsera - aggregated Reddit commentary on the course (praise for practical labs; reports of grading/logistics complaints)
- Class Central - Machine Learning with Python (IBM) course listing and learner reviews
- IBM AI Engineering Professional Certificate (Coursera) - confirms this course is part of the certificate series