Title
Mathematics For Machine Learning
Sold by Ergodebooks, an authorized reseller.
Returns accepted within 30 days | support@ergodebooks.com
Shipping Information
- Free Standard Shipping — United States only
- Processing Time: 1–3 business days
- Estimated Delivery: 3–5 business days after dispatch
- Double-boxed, fully insured & discreetly packaged
- Tracking number sent via email once dispatched
- Orders over $250 require signature upon delivery. Taxes calculated at checkout.
Returns & Refund
Returns accepted within 30 days of delivery.
Damaged or Defective Item
Free return shipping + replacement or full refund
Wrong Item Received
Free return shipping + replacement or full refund
Change of Mind
Return shipping at customer's expense · 25% restocking fee applies
Payment Option
The Fundamental Mathematical Tools Needed To Understand Machine Learning Include Linear Algebra, Analytic Geometry, Matrix Decompositions, Vector Calculus, Optimization, Probability And Statistics. These Topics Are Traditionally Taught In Disparate Courses, Making It Hard For Data Science Or Computer Science Students, Or Professionals, To Efficiently Learn The Mathematics. This Self Contained Textbook Bridges The Gap Between Mathematical And Machine Learning Texts, Introducing The Mathematical Concepts With A Minimum Of Prerequisites. It Uses These Concepts To Derive Four Central Machine Learning Methods: Linear Regression, Principal Component Analysis, Gaussian Mixture Models And Support Vector Machines. For Students And Others With A Mathematical Background, These Derivations Provide A Starting Point To Machine Learning Texts. For Those Learning The Mathematics For The First Time, The Methods Help Build Intuition And Practical Experience With Applying Mathematical Concepts. Every Chapter Includes Worked Examples And Exercises To Test Understanding. Programming Tutorials Are Offered On The Book'S Web Site.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does this book have? A: This book has three hundred ninety-eight pages. It provides comprehensive coverage on the mathematical tools essential for understanding machine learning.
- Q: What type of binding does this book have? A: This book is available in paperback binding. The paperback format makes it portable and easy to handle for students.
- Q: What dimensions does the book have? A: The book measures seven point zero one inches in length, zero point eight eight inches in width, and ten inches in height. These dimensions make it a convenient size for reading.
- Q: What prior knowledge do I need to understand this book? A: A basic understanding of mathematical concepts is recommended. The book introduces concepts with minimal prerequisites, making it accessible to newcomers.
- Q: Is this book suitable for beginners in machine learning? A: Yes, this book is suitable for beginners. It serves as an accessible introduction to the mathematical foundations of machine learning.
- Q: Can I use this book for self-study? A: Yes, this book is ideal for self-study. Each chapter includes worked examples and exercises to test your understanding.
- Q: How do I keep this book in good condition? A: Store the book in a dry, cool place to prevent wear and tear. Avoid exposing it to direct sunlight to maintain its quality.
- Q: Are there any special storage guidelines for this book? A: No special storage guidelines are required. Just ensure it is kept flat or upright on a shelf to avoid bending the pages.
- Q: Is this book safe for all ages? A: Yes, the content is appropriate for all ages. It focuses on mathematics and machine learning concepts without any adult themes.
- Q: What if I find a mistake in the book? A: If you find a mistake, you can contact the publisher for corrections or errata. They usually provide resources to address such issues.
- Q: Can I return the book if I'm not satisfied? A: Yes, you can typically return the book if you're not satisfied, depending on the retailer's return policy. Check their specific guidelines for details.
- Q: What kind of exercises does this book include? A: The book includes various exercises at the end of each chapter. These exercises help reinforce the material and test your comprehension.
- Q: Does the book provide online resources? A: Yes, the book offers programming tutorials on its website. These resources complement the text and provide practical experience.
- Q: Is there an author biography included? A: Yes, the book includes information about the author, Marc Peter Deisenroth. He has expertise in machine learning and mathematics.
- Q: What topics are covered in this book? A: The book covers linear algebra, vector calculus, probability, and optimization, among other essential topics for machine learning.