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  Author: creativelivenew1   |   08 July 2025   |   Comments icon: 0

Free Download Mastering Azure Data Factory for Modern Data Integration
Design, Automate and Build Real-Time Data Integration Pipelines and BI Solutions by Integrating Your Data Workflows with Azure Data Factory

English | 2025 | ISBN: 9789349888814 | 312 pages | True EPUB | 128 MB

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  Author: creativelivenew1   |   08 July 2025   |   Comments icon: 0

Free Download Mastering Advance Data Analytics Machine Learning, Data Mining and Analytic Thinking by Dr. Alex Harper
English | November 15, 2024 | ISBN: N/A | ASIN: B0DN9V6XS2 | 222 pages | EPUB | 0.68 Mb
"Mastering Advanced Data Analytics"

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  Author: creativelivenew1   |   08 July 2025   |   Comments icon: 0

Free Download Mastering AI Prompts: Unlocking the Power of Communication
English | 2025 | ASIN: B0F51T784M | 34 pages | Epub | 1.23 MB
This book is your guide to becoming a prompt virtuoso. Through clear explanations, practical examples, and actionable tips, you'll learn how to get the most out of AI tools - whether your crafting engaging social media posts, streamlining you business operations, or delving into the depths of specialized fields like education and design. Each chapter is designed to not only teach you the fundamentals of good prompts but also to inspire you with the endless possibilities that well-constructed prompts can bring.

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  Author: creativelivenew1   |   08 July 2025   |   Comments icon: 0

Free Download Mastering AI Projects: From Concept to Deployment with Precision and Scale: AI with Python made simple (Future Finance Book 3)
English | 2025 | ASIN: B0DVN4C4WB | 522 pages | PDF | 2.6 MB
Discover the full potential of artificial intelligence with Mastering AI Projects with Python, your comprehensive guide to building, optimizing, and deploying real-world AI solutions. Whether you're an aspiring AI engineer, data scientist, or experienced developer, this book provides practical, hands-on techniques to take AI projects from idea to implementation-with scalable, production-ready code.

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  Author: creativelivenew1   |   08 July 2025   |   Comments icon: 0

Free Download Mass Spectrometry Imaging of Small Molecules: Methods and Protocols
by Young-Jin Lee
English | 2022 | ISBN: 1071620290 | 285 Pages | True ePUB | 52 MB

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  Author: creativelivenew1   |   08 July 2025   |   Comments icon: 0


Free Download Maschinenbau: Ein Lehrbuch für das ganze Bachelor-Studium, 2. Auflage by Werner Skolaut
Deutsch | PDF | 2018 | 1416 Pages | ISBN : 3662558815 | 157.64 MB

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  Author: creativelivenew1   |   08 July 2025   |   Comments icon: 0

Free Download Manifest As You Read: Through Instant Manifestation Protocols by Richard Dotts
English | January 3, 2021 | ISBN: B08S223QMS | 75 pages | MOBI | 35 Mb
Is it really possible to manifestas you read?

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  Author: creativelivenew1   |   08 July 2025   |   Comments icon: 0

Free Download Maintainability of Building Envelope Elements: Optimizing Predictive Condition-Based Maintenance Decisions
by Cláudia Ferreira, Ana Silva
English | 2023 | ISBN: 3031147669 | 299 Pages | True ePUB | 47 MB

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  Author: creativelivenew1   |   08 July 2025   |   Comments icon: 0

Free Download S. Sridhar, "Machine Learning"
English | ISBN: 0190127279 | 2021 | 497 pages | PDF | 76 MB
This book on Machine Learning is designed as a textbook for undergraduate and post-graduate students of engineering. It provides a comprehensive coverage of fundamentals of machine learning. Spread over 16 chapters, the book starts with an overview of machine learning and discusses the need for understanding data and necessary mathematics. It goes on to explain the basics of learning theory, regression analysis, decision tree, and decision rule-based classification algorithms. The book provides an introduction to Bayesian learning and probabilistic graphical models. Important topics such as support vector machines, artificial neural networks, ensemble learning, clustering algorithms, reinforcement algorithms, and genetic algorithms are discussed in depth. It ends with the latest developments in deep learning. A perfect balance between theoretical and mathematical exposition is provided with several numerical examples, review questions, and Python programs. It will also be useful for engineering professionals and IT employees who want to learn the basics of the subject. Key features. Adopts an 'Algorithmic Approach' to illustrate the concepts of machine learning in a simple language with 100+ numerical problems. Adapts 'Minimal Mathematics Strategy' with more emphasis on understanding the basics of machine learning. Has 'Comprehensive Coverage' of all topics that are relevant to machine learning with 100+ figures and Python codes. Provides 'Simple Explanation' to topics such as clustering, support vector machines, genetic algorithms, artificial neural networks, ensemble learning, and deep learning. Contains 'Appendices' that discuss the basics of Python and Python packages such as NumPy, Pandas, Scikit-learn, MatDescriptionlib, SciPy, and Keras. Includes a 'Laboratory Manual' with examples illustrated through Python and its packages. Comes with 'Useful Pedagogical Features' such as Crossword and Word Search Online Resources The following resources are available to support the faculty and students using this book: For faculty:. Chapter PPTs. Solution Manual For students:. Python Programs. Lab Manual. Crosswords and Word Search to understand the subject better Table of contents 1. Introduction to Machine Learning 2. Understanding Data 3. Basics of Learning Theory 4. Similarity-based Learning 5. Regression Analysis 6. Decision Tree Learning 7. Rule-based Learning 8. Bayesian Learning 9. Probabilistic Graphical Models 10. Artificial Neural Networks 11. Support Vector Machines

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  Author: creativelivenew1   |   08 July 2025   |   Comments icon: 0

Free Download Machine Learning for Imbalanced dаta: Tackle imbalanced datasets using machine learning and deep learning techniques by Kumar Abhishek, Dr. Mounir Abdelaziz
English | November 30, 2023 | ISBN: 1801070830 | 344 pages | PDF | 12 Mb
Take your machine learning expertise to the next level with this essential guide, utilizing libraries like imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy to maximize model performance and tackle imbalanced data

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