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Item #:
62638
Machine Learning: The Art and Science of Algorithms that Make Sense of Data 1st Edition
Item #:
62638
AUD 46
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Machine Learning will set a new standard as an introductory textbook.
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What Stands Out
Comprehensive Coverage
This book offers a thorough exploration of machine learning principles, techniques, and algorithms, making it ideal for both beginners and experienced practitioners seeking to deepen their understanding of data-driven decision-making.
Practical Examples
Rich in real-world applications, the book illustrates how algorithms function in various scenarios, enabling readers to grasp complex concepts while providing insights into effective problem-solving strategies in data analysis.
Expert Insights
Authored by leading experts, the text provides valuable perspectives on current trends and future directions in machine learning, equipping readers with knowledge that enhances their skills and marketability in the rapidly evolving tech landscape.
Product Details
- Comprehensive coverage of machine learning concepts with a clear, example-based approach
- Features case studies of increasing complexity and variety with well-chosen examples and illustrations
- Covers a wide range of logical, geometric, and statistical models as well as state-of-the-art topics
- Particular attention paid to the central role played by features
- Balances the use of established terminology with the introduction of new and useful concepts
- Sets a new standard as an introductory textbook in the field of machine learning
| Item Weight | 2 lbs (910 grams) |
Who Should Buy?
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Aspiring Data Scientists
Ideal for beginners seeking a fundamental understanding of machine learning concepts and algorithms in data analysis.
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Academic Researchers
Useful for researchers wanting to explore the theoretical underpinnings and applications of algorithms in various disciplines.
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Industry Professionals
Beneficial for professionals looking to enhance their skills in machine learning and implement practical solutions in their work.
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Complete Beginners
May be too technical for those with no prior knowledge of machine learning or programming concepts and terminology.
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Advanced Practitioners
Lacks depth for experienced data scientists seeking advanced techniques and the latest trends in machine learning.
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Casual Readers
Not suitable for general audiences looking for light reading as it focuses heavily on technical content and algorithms.
Product Description
Machine Learning: The Art and Science of Algorithms that Make Sense of Data 1st Edition
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Customer Questions & Answers
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Question:
What is the core subject of 'Machine Learning: The Art and Science of Algorithms that Make Sense of Data'?
Answer: The book focuses on the principles and methodologies of machine learning, illustrating how algorithms process and learn from data. It delves into various types of algorithms—supervised, unsupervised, and reinforcement learning—showcasing real-world applications in fields like finance, healthcare, and artificial intelligence. By bridging theory and practical use cases, it helps readers understand how to harness the power of data effectively. -
Question:
Who is the intended audience for this book?
Answer: This book is aimed at a diverse audience including students, researchers, and professionals in data science and machine learning. It's particularly valuable for those who possess a foundational understanding of programming and statistics, as it discusses complex algorithms in an accessible manner. Readers looking to enhance their skills or transition into data science will find this resource insightful. -
Question:
What are some key topics covered in the book?
Answer: Key topics include algorithm design, model evaluation, and practical tools for data analysis. It covers essential concepts like decision trees, neural networks, and clustering techniques, providing a holistic view of machine learning. Furthermore, the book discusses challenges faced in data-driven decision making, empowering readers with the knowledge to tackle real-world problems effectively. -
Question:
How does this book differentiate from other machine learning texts?
Answer: This book uniquely combines theoretical frameworks with hands-on applications, making complex concepts comprehensible. Unlike many texts that focus solely on technical details or math, it emphasizes real-world scenarios where these algorithms can be applied. This practical approach helps readers not only learn the 'how' but also understand the 'why' behind machine learning techniques. -
Question:
Are there any coding exercises included in the book?
Answer: Yes, the book includes various coding exercises and examples that facilitate practical learning. These exercises help reinforce understanding by allowing readers to implement algorithms using popular programming languages like Python. By working through these examples, readers can gain confidence in applying machine learning techniques to their own datasets, thus enhancing their hands-on skills. -
Question:
What prerequisites should a reader have before diving into this book?
Answer: Readers are recommended to have a basic understanding of programming and familiarity with statistics and linear algebra. Knowledge of Python or R is beneficial to fully engage with the coding examples presented. This foundational knowledge enables readers to appreciate the algorithms and techniques discussed, ensuring a smoother learning curve. -
Question:
Can this book be used as a textbook for a data science course?
Answer: Absolutely! This book serves as a great textbook for university-level data science courses. Its clear explanations and structured approach make it suitable for classroom settings, offering both theoretical insights and practical exercises. Additionally, instructors can utilize the book as a reference for assigning projects that challenge students to apply machine learning concepts. -
Question:
What is the importance of learning about algorithms in machine learning?
Answer: Understanding algorithms is crucial for developing effective machine learning models that can extract insights from data. Each algorithm has its strengths and weaknesses, and knowing which one to apply under specific conditions can significantly impact the outcome. By mastering the art and science of these algorithms, practitioners can optimize performance across various applications, from predictive analytics to automated decision-making. -
Question:
What real-world applications are discussed in the book?
Answer: The book highlights real-world applications of machine learning in sectors such as healthcare, finance, and e-commerce. For example, it discusses how algorithms are used for diagnosing diseases, optimizing stock trading strategies, and personalizing customer experiences. These case studies demonstrate the practical impact of machine learning, showing readers how to leverage data effectively in various industries. -
Question:
Where can I buy 'Machine Learning: The Art and Science of Algorithms that Make Sense of Data 1st Edition' in Australia?
Answer: You can purchase 'Machine Learning: The Art and Science of Algorithms that Make Sense of Data 1st Edition' on Ubuy in Australia. Ubuy offers a broad selection of products and can provide access to this insightful book, making it easier for you to obtain the resources needed for your machine learning journey.
Computer Vision & Pattern Recognition Editorial Review
The 1st Edition of "Machine Learning" by Peter Flach has garnered mixed reviews from readers. Some readers find the book overly focused on mathematical rigor and lacking in coverage of modern deep learning concepts. They criticize the book for diving into complex jargon without providing much value, particularly for those new to machine learning. The subtitle "the art and science of algorithms that make sense of data" is Considered misleading, as the book is seen as more focused on mathematical foundations rather than practical algorithms. However, on the positive side, some readers appreciate the book's comprehensive coverage of machine learning algorithms, with a detailed division by genre. They find it suitable for teaching purposes and appreciate the different perspective it offers, with a focus on mathematics and statistics rather than business problem-solving. It is also Considered highly readable and praised for effectively explaining complex concepts. Overall, the book is recommended for individuals with a strong mathematical background and those who want to gain a deep understanding of the theoretical concepts behind machine learning algorithms. However, it may not be suitable as a first book for those seeking a more practical, overview of machine learning algorithms and their use cases.
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Pros
- Comprehensive coverage of machine learning algorithms
- Detailed division of algorithms by genre
- Highly readable and well-explained concepts
- Different perspective focusing on mathematics and statistics
Cons
- Overemphasis on mathematical rigor, lacking in coverage of modern deep learning concepts
Product Price History
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AUD 46
Order now and get it around Wednesday, July 08
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Features & Benefits
- Comprehensive machine learning text
- Example-based approach
- Case studies of increasing complexity
- Covering logical, geometric and statistical models
- State-of-the-art topics included
- Emphasis on the central role of features