Mathematics of Machine Learning: Linear Algebra, Calculus and Probability
Mathematics of Machine Learning is a practical math guide for readers building a foundation for machine learning. It covers linear algebra, calculus and probability, with Python implementations intended to connect mathematical ideas to ML applications.
At a glance
- Explains linear algebra topics including vectors, matrices, eigenvalues and matrix factorizations.
- Covers differentiation, integration, gradients and multivariable optimization.
- Introduces probability topics including random variables, distributions, expected value and maximum likelihood estimation.
- The listing describes Python implementations of mathematical concepts and algorithms, including gradient descent.
- The publisher recommends foundational algebra and Python knowledge, plus basic familiarity with machine learning tools.
Reference price: $59.99
Price checked: Nov 14, 2026
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Pros and cons
Pros
- Connects core mathematics to machine learning applications rather than treating the subjects in isolation.
- The stated scope spans linear algebra, calculus and probability, with Python examples to put concepts into practice.
- The listed 730-page length allows room for broad topic coverage.
Technical specifications
8 specifications| Publisher | Packt Publishing |
|---|---|
| Publication date | May 30, 2025 |
| Language | English |
| Print length | 730 pages |
| ISBN-10 | 1837027870 |
| ISBN-13 | 978-1837027873 |
| Weight listed | 2.72 pounds |
|---|---|
| Dimensions | 7.5 x 1.65 x 9.25 inches |
What to know before buying
Listing details to confirm
The publisher's page-length description says approximately 770 pages, while the product details list 730 pages. Confirm the page count for the selected edition. The listing says a print or Kindle purchase includes a free PDF eBook, but does not specify delivery or redemption details.
Who is it for?
A good fit for
- Aspiring machine learning engineers, data scientists, software developers and researchers seeking more mathematical grounding.
- Readers with foundational algebra and Python skills who want to relate math to ML tasks such as gradient descent.
Less suited to
- Readers seeking a purely hands-on machine learning recipe book without substantial mathematics.
- Readers without foundational algebra or Python knowledge, which the publisher recommends before starting.
Customer reviews on Amazon
- 5 stars
71% 71% - 4 stars
13% 13% - 3 stars
6% 6% - 2 stars
2% 2% - 1 star
8% 8%
Customer feedback highlights a solid explanation of underlying math, with favorable remarks about comprehensive coverage and an appendix with resources. The aggregate ratings lean positive, though a notable minority are low. For readers using the book to deepen ML math, the reported detail may be useful, but ratings and comments do not establish that its pace or explanations will suit every background.
Positive themes
- Customers report that the book develops a solid grasp of the underlying mathematical concepts.
- Feedback praises the comprehensive coverage and mentions a helpful appendix with resources.
Frequently asked questions
What subjects does the book cover?
The listed scope includes linear algebra, calculus and probability, with topics such as matrices, eigenvalues, differentiation, gradients, distributions and expected value.
Does it include practical coding?
The listing describes Python implementations of mathematical concepts and algorithms, including gradient descent. It recommends having foundational Python knowledge.
What background does the publisher recommend?
Foundational algebra and Python, along with basic familiarity with machine learning tools.
How long is the book?
Product details list 730 pages, while the publisher's page description calls it approximately 770 pages. Confirm the page count for the selected edition.
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