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Item #:
139778066
Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning
89% of respondents would recommend this to a friend
Item #:
139778066
AUD 97
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What Stands Out
Comprehensive Curriculum
Covers essential topics like linear algebra, calculus, and probability specifically for machine learning applications, ensuring students acquire foundational skills vital for advanced studies and practical implementations.
Practical Applications
Focuses on real-world scenarios where mathematical concepts are applied in machine learning, bridging the gap between theory and practice, thereby enhancing learners' problem-solving abilities.
Expert Guidance
Designed and taught by industry professionals and academics, offering insights into cutting-edge techniques and methodologies, thus providing learners with valuable mentorship and networking opportunities.
Product Details
| Publisher | Packt Publishing |
| Publication date | May 30, 2025 |
| Language | English |
| Print length | 730 pages |
| ISBN-10 | 1837027870 |
| ISBN-13 | 978-1837027873 |
| Item Weight | 2.72 pounds (1.23 kg) |
| Dimensions | 7.5 x 1.65 x 9.25 inches (19.1 x 4.2 x 23.5 cm) |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for those starting a career in data science seeking foundational knowledge in mathematical concepts for machine learning.
-
Undergraduate Students
University students pursuing courses in mathematics, statistics, or computer science will find this material enhances their understanding.
-
Machine Learning Enthusiasts
Individuals interested in deepening their knowledge of machine learning frameworks and algorithms through mathematical principles will benefit.
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Beginners Without Background
Complete beginners with no mathematical foundation may struggle and find the concepts too advanced or overwhelming.
Product Description
Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning
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Mathematical Analysis Editorial Review
Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning is a comprehensive guide that explores the foundational mathematical concepts essential for machine learning. Tivadar Danka offers a deeply insightful approach, emphasizing clarity and practical relevance while covering vital topics such as vectors, matrices, and probability. Testimonials highlight the book's unique ability to present complex ideas clearly, making them accessible even to those with a modest mathematical background. The engaging discussions around topics like the curse of dimensionality and the Monty Hall Problem deepen understanding and intrigue, ensuring that readers come away with both theory and application insights.
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Pros
- Covers foundational math for machine learning clearly.
- Engaging discussions on intriguing concepts like the Monty Hall Problem.
- Cleans up complex topics into accessible insights.
- Well-structured approach from basics to advanced applications.
- Includes practical Python code examples for better understanding.
Cons
- Some readers may find the math challenging at times.
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AUD 97
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