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Item #:
91400209
Machine Learning by Design: Patterns and Principles for Data Collection, Feature Engineering, and Model Development
Item #:
91400209
KWD 17
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This 562-page guide is your companion through the entire ML workflow from data to models.
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What Stands Out
Comprehensive Framework
Delivers a structured approach to machine learning, enabling practitioners to effectively gather data, engineer features, and develop models, enhancing their project success rates.
Practical Insights
Offers actionable insights and best practices derived from real-world applications, ensuring users can overcome common challenges faced in machine learning projects.
User-Centric Design
Focuses on design principles that prioritize user needs, making it easier for data scientists and teams to apply concepts directly to their unique situations.
Product Details
| Publisher | Independently published |
| Publication date | October 9, 2024 |
| Language | English |
| Print length | 562 pages |
| ISBN-13 | 979-8342441940 |
| Item Weight | 2.56 pounds (1.16 kg) |
| Dimensions | 6.93 x 1.27 x 9.84 inches (17.6 x 3.2 x 25 cm) |
Product Description
Machine Learning by Design: Patterns and Principles for Data Collection, Feature Engineering, and Model Development
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Customer Questions & Answers
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Question:
Who is the author of this book?
Answer: The author is Peng Shao, a former Staff ML Engineer at Twitter and a founding member of Alexa. -
Question:
What does this guide cover?
Answer: It covers the entire ML workflow from data collection to model development and includes best practices. -
Question:
Is this book suitable for beginners?
Answer: Yes, it provides foundational principles and guidance that can benefit both beginners and experienced practitioners.
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Pros
- Excellent performance
- Durable and long-lasting
- User-friendly design
- Stylish appearance
- Great customer support
Cons
- A bit heavy for portability.
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Features & Benefits
- Comprehensive 562-page guide on machine learning workflows.
- Covers data collection, feature engineering, and model development.
- Includes 15 timeless principles of machine learning with real-world examples.
- Provides strategies for assembling high-quality datasets.
- Step-by-step guidance for rapid model development and prototyping.
- Best practices for model troubleshooting and tuning with technical details.
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