What topics are covered in 'Fundamentals of Machine Learning for Predictive Data Analytics'?

This book covers a comprehensive range of topics in machine learning, including core algorithms, predictive modeling techniques, feature selection, and data preprocessing. It also delves into specific methods for regression, classification, and clustering, providing worked examples and real-world case studies. By exploring these various aspects, readers gain a solid foundational understanding of how machine learning can be applied to predictive data analytics across different domains, making it invaluable for both students and professionals in the field.

Who is the target audience for this book?

The target audience for this book includes students, academics, and industry professionals keen on enhancing their knowledge in machine learning and predictive analytics. It serves as a resource for undergraduate and graduate courses, as well as a practical reference for practitioners looking to implement machine learning techniques in their work. With the mix of theoretical insights and practical applications, it caters to a wide audience interested in mastering the tools of predictive data analytics.

How does this book help in understanding predictive analytics?

This book facilitates understanding predictive analytics through its structured approach that combines theory with practice. The inclusion of algorithms, worked examples, and case studies allows readers to grasp complex concepts in a simplified manner, reinforcing learning through real-world application. As readers work through the examples, they develop a practical skill set that can be immediately applied to their projects or research, making this book an essential learning tool.

Are there practical examples in the book?

Yes, the book is rich with practical examples that demonstrate the application of various machine learning algorithms in real-life scenarios. Each chapter includes case studies that highlight how predictive analytics can be employed in different industries, such as finance, healthcare, and marketing. These examples not only illustrate the concepts but also provide insights into best practices, enabling readers to visualize how they can implement these techniques in their own work.

What is the difference between supervised and unsupervised learning covered in the book?

The book clearly differentiates between supervised and unsupervised learning. Supervised learning involves training models on labeled data, where the outcomes are known, to predict future outcomes. In contrast, unsupervised learning deals with unlabeled data, focusing on discovering hidden patterns or groupings. This understanding is crucial, as it lays the groundwork for selecting the appropriate machine learning techniques based on the data available, guiding readers in their analytical efforts.

Does the book include discussions on recent developments in machine learning?

Yes, the book includes discussions on recent developments and trends in machine learning such as deep learning and ensemble methods. These sections help readers stay up to date with the rapidly evolving landscape of predictive analytics. Understanding these advancements ensures that readers are aware of the latest tools and methodologies, making them more equipped to tackle contemporary data challenges effectively.

Can beginners understand this book without prior knowledge?

While some prior knowledge of data analysis and statistics may be beneficial, this book is designed to be accessible to beginners. It introduces fundamental concepts gradually and supports learning with clear explanations and examples. Beginners will find the initial chapters particularly useful as they build the foundational knowledge necessary for more advanced topics that follow. It acts as a bridge for those venturing into the field of machine learning and predictive analytics.

What kind of case studies are included in the book?

The book features a variety of case studies across multiple sectors, such as finance, healthcare, and retail. These case studies detail how machine learning techniques have been successfully implemented to solve real-world problems, providing insights into decision-making processes and results. By analyzing these practical applications, readers can learn how to approach their predictive analytics projects, understand challenges faced, and explore effective strategies for implementation.

Is there a focus on implementing algorithms in programming languages?

Yes, the book emphasizes the importance of implementing machine learning algorithms using popular programming languages such as Python and R. It includes code snippets and examples to guide readers through the process of coding these algorithms, making it easier to apply the theories learned in practical situations. This practical programming emphasis equips readers with the necessary skills to execute their own predictive data analytics projects effectively.

كيف تتسوق Fundamentals of Machine Learning for Predictive Data عبر الانترنت من يوباى?

من السهل التسوق في Fundamentals of Machine Learning for Predictive Data عبر الإنترنت من يوباي. كل ما عليك فعله هو البحث عن المنتج واختيار طريقة الشحن الخاصة بك أثناء الدفع وسيتم توصيله الى عنوانك

كم من الوقت يستغرق الحصول على المنتج بعد تقديم الطلب؟

يختلف وقت تسليم المنتج الذي طلبته حسب ما طلبته وطريقة الشحن التي اخترتها. يتم ذكر وقت التسليم المقدر أثناء عملية الدفع ، لذا كن مرتاحًا أثناء التسوق.

عرض الأصل

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

This highly enriching opus brings the concepts to light with industry case studies and best practices, ensuring you'll experience the real-world value and avoid getting lost in abstraction.

منتج #: 6053464

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Q: Where can I buy Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press) in Qatar?

You can purchase 'Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press)' on Ubuy, which offers a convenient platform to find this title in Qatar. Ubuy provides an easy-to-navigate online shopping experience, ensuring you can quickly access this essential resource for your machine learning education or professional development.

88% من المشترين سيوصون بهذا المنتج لصديق

منتج #: 6053464

QAR 305

تفاصيل السعر

باستثناء رسوم الشحن والجمارك ( سيتم احتساب رسوم الشحن والجمارك عند إتمام الشراء )

^{*سيتم استيراد جميع العناصر من أمريكا}

التوافر : متوفر فى المخزون

مستورد من متجر USA

كمية:

اطلب الآن واحصل عليه حول Sunday, أكتوبر 11

معاملة آمنة

أفضل شركائنا اللوجستيين

This highly enriching opus brings the concepts to light with industry case studies and best practices, ensuring you'll experience the real-world value and avoid getting lost in abstraction.

كفالة يو كير:

لا شيء

اختر الباقة

شحن
سريع

استرجاع
مجاني^*

تغليف آمن

منتجات أصلية %100

الامتثال لمعيار PCI DSS

حاصل على شهادة ISO 27001

Note: Step Down Voltage Transformer required for using electronics products of أمريكا store (110-120). Recommended power converters اشتري الآن.

أبرز ما يلفت الانتباه

Comprehensive Coverage

Offers in-depth insights into machine learning algorithms, making complex concepts accessible through simplified explanations, ideal for novices and experienced practitioners alike.

Practical Applications

Includes worked examples and real-world case studies, allowing readers to see machine learning in action and understand its practical implications in diverse scenarios.

User-Friendly Format

Structured for easy navigation, this edition aids self-learners by providing clear illustrations and step-by-step guidance, enhancing the learning experience significantly.

تفاصيل المنتج

Comprehensive introduction to important machine learning approaches for predictive data analytics
Covers both theoretical concepts and practical applications
Detailed treatment of machine learning approaches with explanatory worked examples
Describes four approaches to machine learning: information-based, similarity-based, probability-based, and error-based learning
Includes techniques for evaluating prediction models and two case studies illustrating specific data analytics projects
Suitable for undergraduates in computer science, engineering, mathematics, or statistics, graduate students, and professionals

Publisher	The MIT Press
Publication date	July 24, 2015
Edition	1st
Language	English
Print length	624 pages
ISBN-10	0262029448
ISBN-13	978-0262029445
Item Weight	2.3 pounds (1.04 kg)
Dimensions	7.31 x 1.06 x 9.31 inches (18.6 x 2.7 x 23.6 cm)

من يجب أن يشتري؟

Aspiring Data Scientists
Ideal for those starting in the field who seek foundational knowledge in machine learning and predictive analytics.
Professionals Upskilling
Great for professionals looking to enhance their skills in data analytics through practical algorithms and case studies.
Academics and Researchers
Useful for academics needing comprehensive insights and worked examples for teaching or research in machine learning.

Advanced Practitioners
Not suitable for experienced data scientists who may require more complex and advanced machine learning strategies.

وصف المنتج

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

هل لديك أي استفسار؟ تحدث معنا

أسئلة العملاء & الإجابات

سؤال: كيف تتسوق Fundamentals of Machine Learning for Predictive Data عبر الانترنت من يوباى?

إجابه: من السهل التسوق في Fundamentals of Machine Learning for Predictive Data عبر الإنترنت من يوباي. كل ما عليك فعله هو البحث عن المنتج واختيار طريقة الشحن الخاصة بك أثناء الدفع وسيتم توصيله الى عنوانك
سؤال: هل Fundamentals of Machine Learning for Predictive Data متوفر للتسوق عبر الإنترنت في Qatar؟

إجابه: نعم ، في يوباي Qatar هذا المنتج متاح لك للتسوق بسعر مناسب. Fundamentals of Machine Learning for Predictive Data غير متوفر محليًا ولكن يمكنك الوثوق بنا بخدماتنا للشحن السريع.
سؤال: كم من الوقت يستغرق الحصول على المنتج بعد تقديم الطلب؟

إجابه: يختلف وقت تسليم المنتج الذي طلبته حسب ما طلبته وطريقة الشحن التي اخترتها. يتم ذكر وقت التسليم المقدر أثناء عملية الدفع ، لذا كن مرتاحًا أثناء التسوق.

Intelligence & Semantics Editorial Review

This book offers a comprehensive walkthrough of the CRISP-DM process for machine learning, providing a case study for each chapter. It discusses the strengths and weaknesses of different approaches such as information-, similarity-, probability-, and error-based learning, as well as exploring topics such as entropy, ID3, and distance metrics. However, the book is not recommended for those with an advanced mathematical background, as it primarily focuses on underlying fundamentals and providing a simple and intuitive explanation of machine learning. Overall, it is a solid and practical book, but some readers wished it offered more advanced knowledge.

مراجعات العملاء وتقييماتهم

4.0

1 تقييمات العملاء

5 نجمة

0%
4 نجمة

100%
3 نجمة

0%
2 نجمة

0%
1 نجمة

0%

أضف تقييم لهذا المنتج

شارك أفكارك مع عملاء آخرين

إيجابيات

Comprehensively covers the CRISP-DM process
Provides case studies for each chapter
Offers a simple and intuitive explanation of machine learning
Clearly written and well-structured for data analysis

سلبيات

Not recommended for those with advanced mathematical background

تاريخ سعر المنتج

معلومات مهمة

القيود: بالنسبة للمنتجات التي يتم شحنها دولياً، يُرجى ملاحظة أن أي ضمان من الشركة المصنعة قد لا يكون صالحاً؛ قد لا تتوفر خيارات خدمة الشركة المصنعة؛ قد لا تكون أدلة المنتج والتعليمات وتحذيرات السلامة مكتوبة بلغة بلد المقصد؛ قد لا يتم تصميم المنتجات (والمواد المصاحبة لها) وفقاً لمعايير بلد الوجهة والمواصفات ومتطلبات الملصقات؛ وقد لا تتوافق المنتجات مع الجهد الكهربي المستخدم في بلد الوجهة والمعايير الكهربائية الأخرى (تتطلب استخدام محوّل كهربي أو جهاز تحويل إذا كان ذلك مناسباً). المستلم مسؤول عن ضمان إمكانية استيراد المنتج بشكل قانوني إلى بلد الوجهة. عند الطلب من يوباي أو الشركات التابعة لها، يكون المستلم هو المستورد المسجل ويجب أن يلتزم بجميع القوانين واللوائح الخاصة ببلد الوجهة.
ليست كل المنتجات المدرجة على يوباي معروضة للبيع، لأن يوباي هو محرك بحث عالمي. المنتجات تخضع للوائح التصدير / التجارة.

QAR 305

اطلب الآن واحصل عليه حول Sunday, أكتوبر 11

كمية:

معاملة آمنة

نوفر لك مدفوعات مشفّرة، وحماية متكاملة للمشتري، مع الالتزام بمعايير PCI DSS وشهادة ISO 27001:2022 لضمان أعلى مستويات الأمان في كل عملية شراء.

أفضل شركائنا اللوجستيين

أسرع توصيل عبر الحدود

المميزات والفوائد

Erudite yet real-world relevant
Predictive analytics and machine learning go hand-in-hand
Provides excellent descriptions of key methods used in predictive analytics
Insight into practical applications of these methods
Incremental discussions ranging from anecdotal accounts to more complex concepts
Emphasis on turning a business problem into an analytics solution
Pertinent case studies and illustrations

ضمان Ubuy

تسوّق بثقة مع منتجات أصلية %100، ومدفوعات آمنة متوافقة مع معيار PCI DSS، وحماية بيانات معتمدة وفق ISO 27001، وشحن دولي سريع، وإرجاع مجاني^*، وتغليف آمن لكل طلب.

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

QAR 305

شحن سريع

استرجاع مجاني*

تغليف آمن

منتجات أصلية %100

الامتثال لمعيار PCI DSS

حاصل على شهادة ISO 27001

أبرز ما يلفت الانتباه

تفاصيل المنتج

من يجب أن يشتري؟

وصف المنتج

أسئلة العملاء & الإجابات

Intelligence & Semantics Editorial Review

مراجعات العملاء وتقييماتهم

أضف تقييم لهذا المنتج

إيجابيات

سلبيات

تاريخ سعر المنتج

معلومات مهمة

QAR 305

المميزات والفوائد

ضمان Ubuy

شحن
سريع

استرجاع
مجاني^*