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Machine Learning Engineering
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Machine Learning Engineering
You're looking at one of the few true Applied Machine Learning books out there.
Machine Learning Engineering
Item #: 34764713

Machine Learning Engineering

Item #: 34764713

XOF 45169

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What Stands Out

Print Replica Format
Offers an authentic reading experience by preserving the original layout, making it easier to follow complex diagrams and illustrations crucial for understanding machine learning concepts.
Kindle Accessibility
Accessible on multiple devices, allowing learners to study anytime, anywhere, with features like adjustable text size and built-in dictionary for enhanced comprehension.
Comprehensive Content
Covers essential machine learning principles, practical applications, and industry insights, equipping readers with valuable knowledge to excel in the ever-evolving tech landscape.

Product Details

Explore the best Machine Learning Engineering products at Ubuy Guinea-Bissau Qatar. Browse our wide range of options and shop now for great deals!
Item Weight1.5 lbs (680 grams)

Who Should Buy?

Suitable For
  • Aspiring Engineers

    Perfect for new machine learning engineers seeking structured guidance on practical applications and foundational concepts.

  • Data Scientists

    Ideal for data scientists looking to deepen their understanding of engineering practices in deploying machine learning solutions.

  • Tech Students

    Great resource for students studying data science or AI, providing insights into machine learning engineering methodologies.

Not Suitable For
  • Beginners

    Not suitable for absolute beginners with no background in programming or data science, as it assumes prior knowledge.

  • Casual Readers

    Individuals looking for light reading will find this technical material too dense and detailed for casual consumption.

  • Management Roles

    Professionals in management positions may find the technical focus less applicable to their day-to-day responsibilities.

Product Description

Machine Learning Engineering

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Customer Questions & Answers

  • Question: What is the main focus of the Machine Learning Engineering book?

    Answer: The Machine Learning Engineering book primarily focuses on the practical application of machine learning techniques in engineering projects. It blends theory with hands-on applications, making it ideal for readers looking to implement machine learning in real-world scenarios. By providing case studies and detailed methodologies, the book equips practitioners with the necessary tools to build robust machine learning systems, enabling them to handle data preprocessing, model selection, and deployment effectively.
  • Question: Who is the target audience for this book?

    Answer: The target audience for the Machine Learning Engineering book includes data scientists, software engineers, and students interested in enhancing their understanding of practical machine learning. It’s designed for those who possess a foundational knowledge of machine learning concepts and want to deepen their practical skills. Additionally, professionals looking to implement machine learning solutions in their organizations will find the book particularly useful, as it covers essential principles and best practices.
  • Question: What topics are covered in the Machine Learning Engineering book?

    Answer: The book covers a wide range of topics, including data preprocessing, feature engineering, model selection, and system design. It also delves into deployment strategies, scaling models, and real-time predictions. By covering both the theoretical background and practical implementation, it serves as a comprehensive guide for anyone looking to build and deploy machine learning applications in industry, ensuring readers understand the full lifecycle of machine learning projects.
  • Question: How does this book differ from other machine learning books?

    Answer: This book differentiates itself by focusing explicitly on the engineering aspects of machine learning, rather than just the algorithms or theory. While many machine learning books cover theoretical concepts, this one emphasizes practical applications, providing readers with a robust understanding of deployment and scalability issues. It includes real-world examples and emphasizes best practices, making it a valuable resource for professionals who wish to integrate machine learning into their existing systems.
  • Question: Can beginners benefit from the Machine Learning Engineering book?

    Answer: Yes, while the book is tailored for readers with some foundational knowledge in machine learning, beginners can also benefit, especially those willing to engage with supplementary resources. The initial chapters introduce key concepts in machine learning, guiding readers through various stages of project implementation. Beginners can gain insights into machine learning frameworks and practical applications, assisting them in building a solid foundation and advancing towards more complex projects.
  • Question: Are there any practical projects included in the book?

    Answer: Yes, the Machine Learning Engineering book includes several practical projects and case studies that demonstrate the application of various machine learning techniques. These projects allow readers to apply theoretical knowledge to real-world scenarios, reinforcing their understanding. This hands-on approach helps learners gain valuable experience in building machine learning models, tackling challenges, and developing solutions, ensuring they’re well-prepared for practical implementation.
  • Question: Who are the authors of the Machine Learning Engineering book?

    Answer: The book is authored by recognized experts in the field of machine learning, who bring extensive experience in both theory and practice. Their backgrounds in software engineering, data science, and machine learning applications provide readers with deep insights into the subject matter. The authors' combined expertise ensures the content is reliable, current, and applicable in real-world contexts, making it a trusted resource for professionals and learners alike.
  • Question: What skills will I gain from reading this book?

    Answer: By reading the Machine Learning Engineering book, you will gain valuable skills in designing, building, and deploying machine learning systems. You will learn about data handling, model optimization, and performance evaluation techniques. Additionally, the book teaches you how to overcome common industry challenges, such as scalability and model maintenance, equipping you with practical solutions that can be applied to your own projects in diverse settings, from startups to established enterprises.
  • Question: Where can I buy the Machine Learning Engineering book in Guinea-Bissau?

    Answer: You can buy the Machine Learning Engineering paperback at Ubuy in Guinea-Bissau. Ubuy offers a wide selection of books including this one, making it easy for readers to access and purchase their desired titles online. With a straightforward ordering process and various delivery options, Ubuy ensures you can conveniently get your copy of the book and dive into the world of machine learning engineering.
  • Question: Is there a digital version available for this book?

    Answer: Yes, the Machine Learning Engineering book is available in digital format. This version is convenient for those who prefer reading on e-readers, tablets, or computers. The digital version often allows for features like adjustable text sizes and search functionalities, enhancing the reading experience. Additionally, having a digital copy can facilitate easier annotation and reference, making it an excellent choice for students or professionals on the go.

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XOF 45169

Order now and get it around Monday, August 03

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Features & Benefits

  • Author Andriy Burkov is a leader in the field with a Ph.D. in AI.
  • Features best practices and design patterns for scalable machine learning solutions.
  • Unique focus on practical applications rather than theoretical algorithms.
  • Provides detailed guidance on the end-to-end machine learning process.
  • Address common pitfalls and mistakes in AI system building.
  • Highly recommended by industry leaders, including the Chief Decision Scientist at Google.

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