Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
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Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package.
Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024
Προϊόν #: 112607187

Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024

Προϊόν #: 112607187

€ 72

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What Stands Out

Comprehensive Coverage
Offers a thorough exploration of linear algebra concepts, tailored specifically for applications in data science, machine learning, and signal processing, ensuring a deep understanding for learners.
Practical Applications
Focuses on real-world applications and problem-solving techniques, enabling readers to effectively apply linear algebra techniques in their projects and research across various emerging fields.
Expertly Crafted
Written by industry experts and academics, this resource provides a blend of theory and practice, equipping readers with the high-quality knowledge necessary to excel in modern analytical environments.

Λεπτομέρειες προιόντος

Shop Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024 online at a best price in Greece. 1009418149
Item Weight2 lbs (910 grams)

Who Should Buy?

Suitable For
  • Data Science Students

    Students pursuing data science will benefit from foundational linear algebra skills necessary for data analysis and modeling.

  • Machine Learning Practitioners

    Practitioners looking to deepen their understanding of machine learning algorithms will find valuable insights in this text.

  • Signal Processing Engineers

    Engineers working in signal processing will appreciate the practical applications of linear algebra in their field.

Not Suitable For
  • Beginners in Mathematics

    Complete novices in mathematics may struggle with the complex concepts presented without prior foundational knowledge.

  • Casual Learners

    Individuals seeking a light introduction to linear algebra might find this textbook too dense and technical.

  • Non-Technical Readers

    Readers without a technical background may find the content difficult to understand and apply in practical terms.

ΠΕΡΙΓΡΑΦΗ ΤΟΥ ΠΡΟΪΟΝΤΟΣ

Linear Algebra for Data Science, Machine Learning, and Signal Processing Hardcover – 16 May 2024

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Ερωτήσεις & Απαντήσεις Πελατών

  • ερώτηση: Who is this textbook intended for?

    απάντηση: It is perfect for upper-level undergraduates and first-year graduate students with prior linear algebra knowledge.
  • ερώτηση: Does the book include exercises for students?

    απάντηση: Yes, it includes over 200 multiple-choice questions and homework exercises with solutions.
  • ερώτηση: Are there practical applications included in the book?

    απάντηση: Absolutely, it highlights various applications such as low-rank approximation, logistic regression, and dimensionality reduction.

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Πλεονεκτήματα

  • Comprehensive coverage of topics
  • Accessible to beginners
  • Clear explanations and examples
  • Useful for various applications
  • Well-structured and organized

Μειονεκτήματα

  • Some advanced topics may require further study.

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  • Δεν είναι όλα τα προϊόντα που παρατίθενται στο Ubuy προς πώληση, καθώς το Ubuy είναι μια παγκόσμια μηχανή αναζήτησης. Τα προϊόντα υπόκεινται σε κανονισμούς εξαγωγής/εμπορίου.

€ 72

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Χαρακτηριστικά & Πλεονεκτήματα

  • Engaging textbook designed for upper-level undergraduates and first-year graduate students.
  • Covers both basic and advanced matrix topics relevant to data science and machine learning.
  • Includes 200+ multiple-choice questions for interactive classroom learning.
  • Features practical exercises with solutions available for instructors.
  • Encourages active learning through 'explore' questions and practical Julia code examples.
  • Offers a suite of computational notebooks for hands-on learning experiences.