Mathematics for Machine Learning & Data Science
Udemy · Jon Krohn · Updated
AI Tutor Rating
7.8/10
Duration
22 hours video
Classes
160
Master the math behind ML: linear algebra, calculus, probability, and optimization with Python code examples and exercises.
Mathematics for Machine Learning & Data Science is a 22-hour Udemy course taught by Jon Krohn that builds the quantitative foundation modern ML practitioners need. Spanning 160 lectures, it covers linear algebra, calculus, probability, optimization, and numerical methods, all reinforced with Python code examples and exercises. The course targets learners who already hold basic algebra skills and want to move confidently from surface-level ML tutorials into a deeper, math-grounded understanding of how algorithms actually work.
What you'll learn in Mathematics for Machine Learning & Data Science
Our Review of Mathematics for Machine Learning & Data Science
Mathematics for Machine Learning & Data Science is structured as a comprehensive, bottom-up curriculum that moves from core linear algebra through calculus workflows, probability, and optimization before landing on real-world Python implementation and advanced topics. With 160 lectures spread across 22 hours, the pacing suggests roughly eight to ten minutes per lecture on average, a format that keeps individual concepts digestible while still building genuine cumulative depth. The chapter sequence, from foundational concepts through numerical methods and eventually to deployment considerations, mirrors the actual workflow a practitioner encounters when moving from model design to production, which is a meaningful structural choice rather than a purely academic ordering.
The stated learning outcomes are specific enough to be useful. Mastering linear algebra for machine learning means a learner should be able to reason about matrix operations, vector spaces, and transformations as they appear in neural network weight updates. Applying calculus and optimization to neural networks points directly at backpropagation and gradient descent, the mechanisms that make deep learning trainable. Implementing numerical methods for ML algorithms rounds out the picture by bridging symbolic math and the floating-point realities of actual code. The inclusion of a dedicated Python section and hands-on probability chapter signals that this is not a pure theory course; learners are expected to write and run code alongside the mathematical exposition.
At $14.99 and with a certificate of completion included, Mathematics for Machine Learning & Data Science offers strong value for self-directed learners. The certificate will not carry the weight of a university credential, but it serves as a shareable signal of effort and topic coverage, useful for portfolio pages or LinkedIn profiles. The only meaningful caveat is that the prerequisite of basic algebra is genuinely a floor, not a suggestion; learners without comfort in symbolic manipulation will likely struggle before the calculus and optimization chapters arrive.
Pros and cons of Mathematics for Machine Learning & Data Science
Pros
- Broad, cohesive curriculum covering linear algebra, calculus, probability, optimization, and numerical methods in a single course
- Python code examples and exercises translate abstract math directly into runnable ML-relevant code
- 22 hours across 160 lectures provides enough depth to meaningfully shift a learner's understanding of ML internals
- Certificate of completion included at a $14.99 price point, making the credential-to-cost ratio very favorable
- Chapter progression mirrors real practitioner workflows, moving from theory through numerical methods to deployment considerations
Things to consider
- Basic algebra is listed as the only prerequisite, but the jump to calculus and optimization may feel steep for learners whose algebra is rusty or informal
- Video-only format with exercises means learners who need live feedback, graded assessments, or peer discussion must seek those resources elsewhere
- The advanced topics and deployment chapters suggest breadth over depth at the course's edges, which may leave experienced practitioners wanting more rigor in those sections
Who should take Mathematics for Machine Learning & Data Science?
Mathematics for Machine Learning & Data Science is best suited for aspiring data scientists, ML engineers, and software developers who can handle basic algebra and want to stop treating ML libraries as black boxes. It is particularly well matched to self-paced learners who need a structured, code-accompanied path through the four mathematical pillars of machine learning without enrolling in a full university program.
Course curriculum for Mathematics for Machine Learning & Data Science
Mathematics for Machine Learning & Data Science at a glance
| Provider | Udemy |
|---|---|
| Instructor | Jon Krohn |
| Level | Intermediate |
| Time to complete | 22 hours video |
| Pricing | $14.99 |
| Certificate | Certificate |
| Prerequisites | Basic algebra |
Fit
Best for
Not ideal for
The bottom line on Mathematics for Machine Learning & Data Science
Mathematics for Machine Learning & Data Science delivers a rare combination of breadth and practical grounding for under $15. Jon Krohn's 22-hour curriculum covers the four mathematical pillars of ML with Python reinforcement throughout, making it a credible first serious math course for practitioners. Learners with genuinely weak algebra foundations should shore those up first, but everyone else will find this a high-value, well-sequenced investment in their technical depth.
Mathematics for Machine Learning & Data Science: frequently asked questions
What does Mathematics for Machine Learning & Data Science actually teach you to do?
The course trains you to apply linear algebra, calculus, probability, and optimization in machine learning contexts, specifically to understand neural network mechanics, implement numerical methods for ML algorithms, and write supporting Python code. By the end, you should be able to reason about why ML algorithms behave as they do rather than simply calling library functions.
Is Mathematics for Machine Learning & Data Science suitable for beginners with no calculus background?
The listed prerequisite is basic algebra, so no prior calculus is required. However, the course covers calculus and optimization in meaningful depth, including applications to neural networks. Learners who are completely new to symbolic math may find the pace challenging; solid comfort with algebra and a willingness to pause and re-watch lectures will make the experience much smoother.
Is the Udemy certificate from Mathematics for Machine Learning & Data Science worth anything professionally?
The certificate of completion is included in the $14.99 price and is shareable on LinkedIn or a portfolio. It does not carry university accreditation, but at that price point it represents strong value as a documented signal of topic coverage. Employers in ML and data science typically weigh demonstrated skills and projects more heavily, so pair the certificate with applied work.
How does Mathematics for Machine Learning & Data Science compare to taking a university linear algebra or calculus course for ML purposes?
A university course offers graded assessments, instructor interaction, and formal credit, but Mathematics for Machine Learning & Data Science covers all four relevant mathematical domains, linear algebra, calculus, probability, and optimization, in one place with ML-specific framing and Python code. For practitioners who need applied fluency rather than a transcript entry, the Udemy course is more targeted and far more affordable.
What is the best way to get the most out of Mathematics for Machine Learning & Data Science on Udemy?
Work through the Python exercises alongside each lecture rather than watching passively. The curriculum builds cumulatively, so skipping the numerical methods or probability chapters to reach advanced topics will create gaps. Supplement with practice by implementing small ML algorithms from scratch in Python after each major section, which reinforces both the math and the code simultaneously.