Artificial Intelligence Foundations: Machine Learning
by Kesha Williams · LinkedIn Learning
Our Verdict
Worth it — with caveatsWorth it for the specific learner: a fast, well-structured 1h 56m primer on the machine learning lifecycle from Kesha Williams (released May 30, 2023), holding a 4.6/5 rating from 7,031 LinkedIn Learning ratings. One correction to the catalog blurb: this is NOT a purely 'non-technical' overview. While the conceptual narration is accessible to non-coders, the course includes Python/Jupyter demos and exercise files on GitHub (a crime-classification model, a home-price regression, and an end-to-end pipeline), so it sits at the boundary between conceptual literacy and light hands-on practice. It is a strong orientation course that names the right concepts (supervised/unsupervised, feature engineering, classification vs. regression metrics, confusion matrix, bias) but is too short and too shallow to make you job-ready in ML. Treat it as a map, not the territory.
Take it if you want a credible, concise orientation to how ML projects actually work and you can access LinkedIn Learning cheaply (free 1-month trial or free via a public-library card). Skip paying a standalone subscription just for this: at under two hours it is an overview, the demos are watch-along rather than build-from-scratch, and free deeper alternatives exist. Its value is conditional on (a) cheap access and (b) realistic expectations that it is a primer.
Best for: Business professionals, product managers, analysts, executives, and aspiring data practitioners who need to understand the ML lifecycle and vocabulary quickly and speak credibly with technical teams. Also a fair pick for total beginners who want a structured first tour before committing to a long, math-heavy course, and for people who already pay for LinkedIn Learning or can access it free through their employer or library.
Skip if: Anyone seeking job-ready ML engineering depth, rigorous math (linear algebra, calculus, the mechanics behind algorithms), or extensive hands-on coding from scratch, this is far too short and the demos are demonstrative, not project-based. Also not ideal for those who refuse a subscription model and want fully free materials (Google's ML Crash Course or Kaggle are better fits), or experienced practitioners who already know the lifecycle.
About This Course
Non-technical overview of ML concepts including supervised learning, unsupervised learning, and model evaluation for business.
What You'll Learn
Curriculum
Course intro and a walkthrough of the scenarios used throughout (~3 min total).
Exploring ML, how machines learn, and why traditional ML still matters.
Breaking down the ML lifecycle, framing ML problems, identifying a pre-built model, and tools used to train a model.
Obtaining data, visualizing and understanding it, feature engineering, plus a hands-on feature-engineering demo.
Learning algorithms and model training; classification vs. regression algorithms; training a custom model with a guided demo.
Classification metrics, the confusion matrix, regression metrics, feature importance, and combating bias.
Structuring an ML pipeline plus a demo designing and building one.
Your machine learning journey and a bridge to generative AI.
Prerequisites
- No prior machine learning experience required; pitched at beginner level
- Basic comfort reading Python is helpful (not mandatory) to follow the Jupyter notebook demos and use the GitHub exercise files
- General data/business literacy helps you connect concepts to real use cases
Instructor
Kesha Williams
Instructor · LinkedIn Learning
Pros & Cons
Pros
- Tightly structured and time-efficient: a complete, logically sequenced tour of the ML lifecycle in under two hours with no filler
- Taught by a credible practitioner (Kesha Williams, a senior enterprise architecture/engineering leader who has authored 25+ courses for 1M+ learners)
- More hands-on than a typical 'foundations' course: real Python/Jupyter demos and downloadable GitHub exercise files (crime classification, home-price regression, pipeline) you can run yourself
- Strong, validated reception: 4.6/5 from 7,031 ratings on LinkedIn Learning, corroborated by a 4.7/5 figure listed on Class Central
- Comes with a shareable LinkedIn Certificate of Completion that posts directly to your profile
Cons
- Breadth over depth: at ~1h 56m it is an overview; concepts are named and illustrated but not deeply explained, and there is little of the underlying math
- Catalog/marketing framing as 'non-technical' is misleading; the demos assume some Python comfort, while the conceptual parts may feel thin to those wanting rigor, an awkward middle for both audiences
- Locked behind a LinkedIn Learning subscription ($29.99/mo or annual); not standalone free, though a free trial and library access exist
- Will not make you job-ready in ML and offers no graded assessments or capstone, you watch demos rather than build projects end-to-end
Alternatives To Consider
Frequently Asked Questions
Is Artificial Intelligence Foundations: Machine Learning free?
Artificial Intelligence Foundations: Machine Learning is $29.99/mo. Requires a LinkedIn Learning subscription, about $29.99/month (cheaper annually) bundled with LinkedIn Premium Career. A 1-month free trial is available, and many public libraries provide LinkedIn Learning free with a library card, the most cost-effective way to take a single short course. Includes a shareable Certificate of Completion.
Who is Artificial Intelligence Foundations: Machine Learning for?
Business professionals, product managers, analysts, executives, and aspiring data practitioners who need to understand the ML lifecycle and vocabulary quickly and speak credibly with technical teams. Also a fair pick for total beginners who want a structured first tour before committing to a long, math-heavy course, and for people who already pay for LinkedIn Learning or can access it free through their employer or library.
What will you learn in Artificial Intelligence Foundations: Machine Learning?
How machines learn and where traditional ML still matters versus newer approaches; The end-to-end machine learning lifecycle, including framing an ML problem and deciding between building a model or using a pre-built one; Data preparation fundamentals: sourcing data, visualizing/understanding it, and feature engineering (with a hands-on demo); How to train a custom model and distinguish learning algorithms for classification versus regression.
What are the prerequisites for Artificial Intelligence Foundations: Machine Learning?
No prior machine learning experience required; pitched at beginner level; Basic comfort reading Python is helpful (not mandatory) to follow the Jupyter notebook demos and use the GitHub exercise files; General data/business literacy helps you connect concepts to real use cases.
Is Artificial Intelligence Foundations: Machine Learning worth it?
Take it if you want a credible, concise orientation to how ML projects actually work and you can access LinkedIn Learning cheaply (free 1-month trial or free via a public-library card). Skip paying a standalone subscription just for this: at under two hours it is an overview, the demos are watch-along rather than build-from-scratch, and free deeper alternatives exist. Its value is conditional on (a) cheap access and (b) realistic expectations that it is a primer.
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
- LinkedIn Learning - official course page (live version, ID 22345868): full syllabus, 1h 56m, released May 30, 2023, 4.6/5 from 7,027 ratings, certificate
- GitHub - LinkedInLearning official exercise-files repo: confirms hands-on Jupyter notebooks (crime model, home prediction, pipelines) and course scope
- Class Central - course listing with cross-check rating (4.7/5, 3,421 ratings) and instructor/audience detail
- DigitalOcean - 'Top LinkedIn Learning AI Courses' roundup: independent context on the course's audience and strengths