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Machine Learning: The Basics (Machine Learning: Foundations, Methodologies, and Applications)
85% of respondents would recommend this to a friend
XOF 35169
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To make optimal use of ML, it is essential to understand its underlying principles.
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Product Details
| Item Weight | 1 lbs (450 grams) |
Who Should Buy?
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Beginner Enthusiasts
Individuals new to machine learning seeking a comprehensive introduction to fundamental concepts and techniques in the field.
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Academic Students
Students enrolled in data science or computer science programs needing a foundational resource for coursework and projects.
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Professionals Transitioning
Industry professionals aiming to pivot into machine learning roles, requiring basic knowledge before advancing to complex topics.
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Advanced Practitioners
Experienced machine learning practitioners may find the content too basic and lacking in advanced methodologies or applications.
Product Description
Machine Learning: The Basics (Machine Learning: Foundations, Methodologies, and Applications)
Customer Questions & Answers
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Question:
Who is this book intended for?
Answer: This book is suitable for beginners and professionals across various fields of science and engineering. -
Question:
What key concepts does the book cover?
Answer: It covers the principles of machine learning, including data, model, loss, regularization, privacy-preservation, and explainability. -
Question:
How can this book help with ML applications?
Answer: It trains readers to break down ML applications into manageable components, helping them choose appropriate methods.
Intelligence & Semantics Editorial Review
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- A bit bulky for smaller spaces.
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XOF 35169
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Features & Benefits
- Essential guide to understanding machine learning principles.
- Comprehensive coverage of ML methodologies and applications.
- Focus on data, model, and loss for effective ML implementation.
- Equips readers with tools to select the right ML methods.
- Ideal for both beginners and professionals in various fields.
- Promotes understanding of key concepts like regularization and privacy-preservation.
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