Machine Learning with Python: Foundations
by Frederick Nwanganga · LinkedIn Learning
Our Verdict
Worth it — with caveatsMachine Learning with Python: Foundations is a strong conceptual primer that earns its 'foundations' title but delivers far less hands-on scikit-learn modeling than its marketing implies. This short (1h 54m), genuinely beginner-friendly course by Frederick (Fred) Nwanganga — a PhD-credentialed Associate Teaching Professor in Notre Dame Mendoza's IT, Analytics, and Operations department and co-author of Wiley's 'Practical Machine Learning in R' — holds a 4.7/5 rating from 6,341 ratings (77% five-star) on LinkedIn Learning, with learners consistently praising its clear, concise explanations of core ML concepts and the end-to-end data workflow. The honest caveat: despite a catalog description implying deep modeling, evaluation, and feature selection, the actual syllabus is concept-and-data-preparation heavy (collecting, summarizing, visualizing, cleaning, normalizing, and sampling data) and builds only a single simple model near the end. It is best treated as a confidence-building first step before a deeper course, not a standalone path to building production ML models.
A high-quality, well-rated foundational primer that delivers exactly what its title promises (foundations) but far less than its catalog description implies. Worth taking if you already subscribe to LinkedIn Learning and want a gentle ~2-hour conceptual on-ramp; not worth a paid subscription on its own, since it teaches little practical scikit-learn modeling and ends after building just one model.
Best for: Complete newcomers to machine learning who know basic Python and want a short, clearly-explained mental model of what ML is, the supervised/unsupervised/reinforcement distinctions, and the full data-to-model workflow before committing to a longer, more technical course. Also a good fit for analysts, managers, or career-switchers who already have LinkedIn Learning access (or LinkedIn Premium) and want a low-commitment, certificate-bearing introduction.
Skip if: Anyone seeking deep, hands-on scikit-learn practice, model evaluation metrics, hyperparameter tuning, or feature-selection technique (the course only lightly touches model building). Intermediate practitioners, people who already understand the ML pipeline, and learners wanting a rigorous math foundation or a portfolio-ready project should skip it and go straight to a deeper course.
About This Course
Build ML models with scikit-learn covering data preparation, model training, evaluation, and feature selection.
What You'll Learn
Curriculum
Sets context: machine learning in our world, what you should know, the tools you need, and how to use the exercise files (~3.5 min total across 4 clips).
Conceptual grounding: what is / is not machine learning, unsupervised vs. supervised vs. reinforcement learning, and the overall steps to machine learning (6 videos, ~28 min).
Things to consider when collecting data and how to import data in Python (2 videos, ~9 min).
Describe, summarize, and visualize your data, with hands-on Python examples for summarizing and visualizing (4 videos, ~20 min).
Common data-quality issues plus how-to-in-Python segments for resolving missing data, normalizing, and sampling, and a concept lesson on dimensionality reduction (7 videos, ~35 min) — the most hands-on part of the course.
Classification vs. regression, how to build a machine learning model in Python, and an overview of common machine learning algorithms (3 videos, ~15 min).
Next steps with applied machine learning (~3 min).
Prerequisites
- Basic Python familiarity (variables, functions, importing libraries) — the course assumes you can read and run simple Python
- Comfort with running Jupyter/Python exercise files; no prior machine learning knowledge required
- A LinkedIn Learning subscription, LinkedIn Premium, or the 1-month free trial to access the videos and exercise file
Instructor
Frederick Nwanganga
Instructor · LinkedIn Learning
Pros & Cons
Pros
- Highly credible instructor: Fred Nwanganga, PhD (Computer Science & Engineering, Notre Dame), is an Associate Teaching Professor in Notre Dame Mendoza's IT, Analytics, and Operations department and co-author of Wiley's 'Practical Machine Learning in R' — verifiable, real-world ML teaching credentials
- Strong, consistent learner reception: 4.7/5 from 6,341 ratings on LinkedIn Learning with 77% five-star, and feedback repeatedly cites clear, concise, well-paced explanations
- Excellent use of ~2 hours: tightly scoped, jargon-light conceptual coverage of the full ML workflow that genuinely lowers the barrier for true beginners
- Practical, accessible data-preparation segments (importing, summarizing, visualizing, handling missing values, normalizing, sampling) with downloadable exercise file and 5 quizzes
- Shareable certificate of completion and mobile access, useful for LinkedIn profiles and learning on the go
Cons
- Misleading depth expectation: the catalog/marketing framing of scikit-learn model training, evaluation, and feature selection overstates reality — the course is concept-and-data-prep focused and builds only one simple model near the end
- Very limited hands-on modeling: almost no coverage of model evaluation metrics, cross-validation, tuning, or comparing algorithms, so you finish unable to build and assess a real model independently
- Not standalone for career goals: requires a paid LinkedIn Learning subscription (or Premium) yet delivers only an orientation, making it poor value unless you already have access or pair it with a deeper course
- Dated content: released October 2021 and not substantially refreshed, so library/API specifics may lag current scikit-learn and Python practice
Alternatives To Consider
Frequently Asked Questions
Is Machine Learning with Python: Foundations free?
Machine Learning with Python: Foundations is $29.99/mo. No standalone price — access requires a LinkedIn Learning subscription (about $39.99/month, or ~$19.99/month on the ~$239.88 annual plan as of 2026), a 1-month free trial, or LinkedIn Premium (which includes Learning at no extra cost). The catalog's '$29.99/mo' reflects an older/promotional rate. A certificate of completion is included.
Who is Machine Learning with Python: Foundations for?
Complete newcomers to machine learning who know basic Python and want a short, clearly-explained mental model of what ML is, the supervised/unsupervised/reinforcement distinctions, and the full data-to-model workflow before committing to a longer, more technical course. Also a good fit for analysts, managers, or career-switchers who already have LinkedIn Learning access (or LinkedIn Premium) and want a low-commitment, certificate-bearing introduction.
What will you learn in Machine Learning with Python: Foundations?
What machine learning is (and what it is not), plus the differences between supervised, unsupervised, and reinforcement learning; The end-to-end steps of a machine learning workflow, from problem framing through to model interpretation; How to collect and import data into Python for a machine learning project; How to describe, summarize, and visualize a dataset in Python to understand it before modeling.
What are the prerequisites for Machine Learning with Python: Foundations?
Basic Python familiarity (variables, functions, importing libraries) — the course assumes you can read and run simple Python; Comfort with running Jupyter/Python exercise files; no prior machine learning knowledge required; A LinkedIn Learning subscription, LinkedIn Premium, or the 1-month free trial to access the videos and exercise file.
Is Machine Learning with Python: Foundations worth it?
A high-quality, well-rated foundational primer that delivers exactly what its title promises (foundations) but far less than its catalog description implies. Worth taking if you already subscribe to LinkedIn Learning and want a gentle ~2-hour conceptual on-ramp; not worth a paid subscription on its own, since it teaches little practical scikit-learn modeling and ends after building just one model.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on LinkedIn Learning'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 course page — syllabus, duration, level, instructor, rating (LinkedIn Learning)
- Class Central course listing & rating reference
- Instructor faculty profile — Frederick Nwanganga, Notre Dame Mendoza College of Business
- Instructor's book — 'Practical Machine Learning in R' (Wiley), corroborating ML authoring credentials
- LinkedIn Learning 2026 pricing reference (monthly/annual/free trial)