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Item #:
1583506
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
90% of respondents would recommend this to a friend
Item #:
1583506
€ 154
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What Stands Out
Comprehensive Coverage
Offers an extensive exploration of machine learning from a probabilistic viewpoint, catering to both beginners and advanced practitioners seeking in-depth knowledge.
Illustrated Content
Includes high-quality illustrations that enhance understanding of complex concepts, making the material more accessible and engaging for visual learners.
Adaptive Learning
Equips readers with adaptive computational techniques that can be applied to real-world problems, ensuring practical application of theoretical knowledge.
Product Details
- Comprehensive introduction to machine learning using probabilistic models and inference
- Focuses on automated methods of data analysis for detecting patterns and predicting future data
- Covers necessary background material on probability, optimization, and linear algebra
- Discusses recent developments in the field including conditional random fields and deep learning
- Emphasizes principled model-based approach using graphical models
- Includes color images, worked examples, and pseudo-code for algorithms; suitable for upper-level undergraduates and beginning graduate students
| Publisher | The MIT Press |
| Publication date | August 24, 2012 |
| Edition | Illustrated |
| Language | English |
| Print length | 1104 pages |
| ISBN-10 | 0262018020 |
| ISBN-13 | 978-0262018029 |
| Item Weight | 4.14 pounds (1.88 kg) |
| Dimensions | 8.27 x 1.77 x 9.25 inches (21 x 4.5 x 23.5 cm) |
Who Should Buy?
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Graduate Students
Ideal for graduate students pursuing advanced studies in machine learning, offering comprehensive insights and probabilistic approaches.
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Data Scientists
Perfect for data scientists looking to deepen their understanding of probabilistic models and apply them in real-world scenarios.
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Research Professionals
Suitable for researchers in AI and machine learning who need a detailed reference for probabilistic techniques and theories.
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Beginners
Not ideal for beginners due to its complex concepts and mathematical rigor, which may be overwhelming without prior knowledge.
Product Description
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
About This Item
Introducing "Machine Learning: A Probabilistic Perspective" - Your Comprehensive Guide to Mastering Machine Learning Concepts and Techniques! Are you keen to delve deep into the world of machine learning? Look no further! Our highlighted product, "Machine Learning: A Probabilistic Perspective," is the ultimate resource you need to understand the intricacies of this cutting-edge technology. Written by renowned experts in the field, this illustrated edition of the popular "Machine Learning: A Probabilistic Perspective" book is part of the Adaptive Computation and Machine Learning series. It explores the key principles, algorithms, models, and applications of machine learning. Whether you’re a beginner or an advanced learner seeking to expand your knowledge, this book has got you covered. This comprehensive guide covers a wide range of topics, including introduction to machine learning, advanced machine learning concepts, and the implementation of machine learning algorithms.
It is designed to provide you with a strong foundation in machine learning theory and equip you with the practical tools necessary for real-world applications. With the help of clear illustrations and examples, you'll gain a deeper understanding of machine learning frameworks and libraries, enabling you to confidently tackle machine learning challenges. This book also discusses the latest trends in the field, keeping you up-to-date with the advancements in machine learning research. Are you interested in applying machine learning to specific domains? "Machine Learning: A Probabilistic Perspective" offers insights into machine learning applications in areas such as healthcare, finance, marketing, e-commerce, computer vision, and more. You'll discover how machine learning techniques can be employed for data analysis, predictive modeling, pattern recognition, natural language processing, image classification, and even fraud detection. Whether you're an aspiring data scientist, a software engineer, or a business professional looking to harness the power of machine learning, this book is an indispensable resource.
It equips you with the knowledge and skills to develop effective machine learning solutions that can revolutionize your industry. Don't miss out on this opportunity to master machine learning! Get your hands on "Machine Learning: A Probabilistic Perspective" now and unlock the endless possibilities of this transformative technology. Start your journey towards becoming a machine learning expert today!.
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Product Buying Guide
Machine learning is a rapidly evolving field that has the potential to revolutionize the way data is analyzed and patterns are predicted. 'Machine Learning: A Probabilistic Perspective' offers a comprehensive introduction to machine learning, using probabilistic models and inference as a unifying approach. This buying guide will provide a detailed overview of the product, its key features, usage scenarios, comparative analysis, user reviews, price analysis, and important buying considerations to help you in making an informed decision.
Product Specifications
- Title: Machine Learning: A Probabilistic Perspective
- Series: Adaptive Computation and Machine Learning Series
- Edition: Illustrated Edition
- Coverage: Probability, Optimization, Linear Algebra, Conditional Random Fields, L1 Regularization, Deep Learning
- Accessibility: Informal, Accessible Style, Pseudo-code for Algorithms, Color Images, Worked Examples
- Implementation: MATLAB Software Package - PMTK (Probabilistic Modeling Toolkit)
- Suitability: Upper-level Undergraduates, Beginning Graduate Students
Key Features
- Comprehensive Introduction to Machine Learning
- Unified, Probabilistic Approach
- In-depth Coverage of Probability, Optimization, and Linear Algebra
- Discussion of Recent Developments such as Conditional Random Fields, L1 Regularization, and Deep Learning
- Emphasis on Model-based Approach
- Implementation in MATLAB Software Package - PMTK
Usage Scenarios
- Automated Data Analysis for Web-enabled Electronic Data
- Detection of Patterns in Data
- Prediction of Future Data
- Application Domains: Biology, Text Processing, Computer Vision, Robotics
Usage Scenarios
Several other renowned textbooks and resources exist in the field of machine learning, offering diverse perspectives and methodologies.
Some User Review
- Users have lauded the book for its comprehensive coverage of machine learning concepts in an accessible manner.
- The inclusion of pseudocode for important algorithms and the use of color images and worked examples have been particularly appreciated by users.
- The application domains provided as examples have helped users in understanding the practical relevance of the concepts discussed.
Competitors
The price of the product is competitive as compared to other similar offerings in the market, considering the depth of coverage and the implementation resources provided.
Buying Considerations
- Consider your familiarity with probability, optimization, and linear algebra concepts as the book delves into these areas as well.
- Evaluate the suitability of the MATLAB software package - PMTK for your implementation needs and compatibility with your existing tools.
- Assess your academic background and level to ensure that the book aligns with your knowledge and learning goals.
Conclusion
Machine Learning: A Probabilistic Perspective provides a valuable blend of theoretical understanding and practical implementation, making it an excellent resource for students and professionals looking to delve into the intricacies of modern machine learning. With its comprehensive coverage of probabilistic models and inference, accessible style, and practical examples, it empowers learners to grasp the fundamental concepts and methodologies in the field.
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Linear Editorial Review
This book is often referred to as the bible of machine learning for good reason. It covers all topics in Considerable depth, providing a solid foundation for anyone looking to begin real work in ML. The author's mathematical explanations are clear and easy to follow, although it should be noted that a reasonably advanced background in estimation theory is required to get the most out of this book. The visual illustrations do an excellent job of explaining concepts that can otherwise be challenging to grasp. The content is heavy on theory, making it more of a reference book than a quick read.
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Pros
- Thorough coverage of all topics
- Clear mathematical explanations
- Excellent visual illustrations
Cons
- Heavy on theory
Product Price History
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