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Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know

Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know

GNNs, PyTorch Geometric, Supervised, Unsupervised, and Deep Learning Algorithms — Set of 2 Books

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Description

Build a Strong Foundation in Modern Machine Learning

Build a strong foundation in modern machine learning with this practical 2-book collection—whether you're just starting out or looking to deepen your technical expertise in AI.

Book 1 – Graph Machine Learning Essentials

  • Understand graph fundamentals, node embeddings, and message passing from basic concepts to practical implementation
  • Build Graph Neural Networks with PyTorch Geometric and learn about scalability, oversmoothing, and real-world challenges
  • See how graph machine learning applies to social networks, fraud detection, recommender systems, bioinformatics, and knowledge graphs

Book 2 – Machine Learning Essentials You Always Wanted to Know

  • Discover what machine learning is, how it evolved, and where it shows up in everyday products
  • Learn the ML workflow—data, models, training, and evaluation—with beginner-friendly explanations
  • Explore supervised and unsupervised learning, plus an introduction to deep learning

Together, these books equip you with both foundational knowledge and specialized skills in graph-based AI, positioning you to advance your career in machine learning, data science, and artificial intelligence.

Bibliographic Details

Pages: 476 pages

Paperback (ISBN): 9781636517766

Category: Business & Economics

Author: Dhairya Parikh, Pintu Kumar, Vibrant Publishers

Table of Contents

Click on individual book titles below to view their complete Table of Contents. 

Machine Learning Essentials You Always Wanted to Know

Graph Machine Learning Essentials

Author

Dhairya Parikh is a seasoned data engineer, a graduate of the University of Waterloo, and a technical writer with expertise in AI, data science, and practical ML applications.

Pintu Kumar is a Ph.D. scholar at IIT Bombay specializing in graph machine learning. A PMRF fellow and Silver Medalist in Mathematics, he focuses on research, teaching, and making complex ideas accessible.

Vibrant Publishers is focused on presenting the best texts for learning about technology and business as well as books for test preparation. Categories include programming, operating systems and other texts focused on IT. In addition, a series of books helps professionals in their own disciplines learn the business skills needed in their professional growth.

Vibrant Publishers has a standardized test preparation series covering the GMAT, GRE and SAT, providing ample study and practice material in a simple and well organized format, helping students get closer to their dream universities.

Series

The Self-Learning Management Series is designed to help students, new managers, career switchers, and entrepreneurs learn essential management lessons and covers every aspect of business, from HR to Finance to Marketing to Operations across any and every industry. Each book includes basic fundamentals, important concepts, and standard and well-known principles as well as practical ways of application of the subject matter.

  • 5 stars: 28 (70%)
  • 4 stars: 12 (30%)
  • 3 stars: 0 (0%)
  • 2 stars: 0 (0%)
  • 1 star: 0 (0%)
S
Swati Mahajan (India)
Masterful breakdown of graph neural networks, embeddings, and analytics

An exceptional publishing achievement by Vibrant Publishers. Every single chapter delivers high-yield takeaways for modern technical practitioners.

V
Vikramaditya Roy (India)
Great investment of time for graph machine learning and AI mastery

This publication helped us overhaul our network data pipelines and deploy robust graph machine learning models. Highly recommended!

S
Suresh Menon (India)
An essential reference book for AI professionals and machine learning learners

Extremely helpful for understanding how to leverage graph machine learning to solve complex relational problems and enhance predictive models.

S
Shreya Shrivastava (India)
Practical frameworks and code snippets for modern graph machine learning

The quality of content is top-notch. Real-world case studies, architectural checklists, and Python code snippets make this an essential desk companion.

S
Sameer Pande (India)
Very well structured book explaining graph representation learning and tools

As a Senior AI Engineer, this manual gave me exact model architectures, graph representation workflows, and analytical toolkits needed to build advanced systems with precision.

R
Rakesh Dwivedi (India)
Highly recommended book focusing on graph algorithms and predictive modeling

Purchased this book to upskill our AI and data science teams. The practical frameworks on node embeddings, link prediction, and network analysis have been immensely valuable.

R
Ritika Sengupta (India)
Exceptional guide offering deep insights into graph machine learning and AI

A remarkably clear and well-structured guide. It bridges complex graph theory and machine learning concepts into digestible, actionable chapters.

R
Rajeshwari Iyer (India)
Invaluable resource collection for data scientists and AI engineers

This book is an absolute masterpiece for professionals wanting to master graph machine learning. The integration of graph neural networks with practical Python implementations is phenomenal.

P
Priyanka Swaminathan (India)
Comprehensive book for mastering graph neural networks and advanced machine learning

This publication helped us overhaul our network data pipelines and deploy robust graph machine learning models. Highly recommended!

P
Pranav Hegde (India)
Phenomenal essential guide covering graph machine learning and network analysis

An exceptional publishing achievement by Vibrant Publishers. Every single chapter delivers high-yield takeaways for modern technical practitioners.

K
Karanjit Singh (India)
Clear guidance on leveraging graph algorithms and neural networks for insights

The quality of content is top-notch. Real-world case studies, architectural checklists, and Python code snippets make this an essential desk companion.

N
Neha Kothari (India)
Superb publication offering unmatched graph machine learning and AI wisdom

Extremely helpful for understanding how to leverage graph machine learning to solve complex relational problems and enhance predictive models.

H
Harshita Das (India)
Top tier reference library for contemporary graph machine learning and AI

As a Senior AI Engineer, this manual gave me exact model architectures, graph representation workflows, and analytical toolkits needed to build advanced systems with precision.

G
Gaurav Choudhury (India)
Transformed how our AI team approaches network data and machine learning

A remarkably clear and well-structured guide. It bridges complex graph theory and machine learning concepts into digestible, actionable chapters.

D
Deepika Menon (India)
Thorough coverage of relational data, node classification, and link prediction

Purchased this book to upskill our AI and data science teams. The practical frameworks on node embeddings, link prediction, and network analysis have been immensely valuable.

A
Abhishek Tiwari (India)
An essential reference book for AI professionals and machine learning learners

An exceptional publishing achievement by Vibrant Publishers. Every single chapter delivers high-yield takeaways for modern technical practitioners.

A
Amitabh Ghosh (India)
Great investment of time for graph machine learning and AI mastery

This book is an absolute masterpiece for professionals wanting to master graph machine learning. The integration of graph neural networks with practical Python implementations is phenomenal.

A
Akanksha Roy (India)
Masterful breakdown of graph neural networks, embeddings, and analytics

This publication helped us overhaul our network data pipelines and deploy robust graph machine learning models. Highly recommended!

Z
Zoya Khan (India)
Practical frameworks and code snippets for modern graph machine learning

Extremely helpful for understanding how to leverage graph machine learning to solve complex relational problems and enhance predictive models.

V
Varun Saxena (India)
Very well structured book explaining graph representation learning and tools

The quality of content is top-notch. Real-world case studies, architectural checklists, and Python code snippets make this an essential desk companion.

T
Tanvi Bhatia (India)
Exceptional guide offering deep insights into graph machine learning and AI

As a Senior AI Engineer, this manual gave me exact model architectures, graph representation workflows, and analytical toolkits needed to build advanced systems with precision.

S
Siddharth Nambiar (India)
Highly recommended book focusing on graph algorithms and predictive modeling

A remarkably clear and well-structured guide. It bridges complex graph theory and machine learning concepts into digestible, actionable chapters.

S
Sanya Mukherjee (India)
Invaluable resource collection for data scientists and AI engineers

Purchased this book to upskill our AI and data science teams. The practical frameworks on node embeddings, link prediction, and network analysis have been immensely valuable.

R
Riya Chatterjee (India)
Phenomenal essential guide covering graph machine learning and network analysis

This publication helped us overhaul our network data pipelines and deploy robust graph machine learning models. Highly recommended!

R
Rohan Pillai (India)
Comprehensive book for mastering graph neural networks and advanced machine learning

This book is an absolute masterpiece for professionals wanting to master graph machine learning. The integration of graph neural networks with practical Python implementations is phenomenal.

R
Rahul Kulkarni (India)
Superb publication offering unmatched graph machine learning and AI wisdom

An exceptional publishing achievement by Vibrant Publishers. Every single chapter delivers high-yield takeaways for modern technical practitioners.

P
Pooja Deshmukh (India)
Clear guidance on leveraging graph algorithms and neural networks for insights

Extremely helpful for understanding how to leverage graph machine learning to solve complex relational problems and enhance predictive models.

N
Nikhil Reddy (India)
Top tier reference library for contemporary graph machine learning and AI

The quality of content is top-notch. Real-world case studies, architectural checklists, and Python code snippets make this an essential desk companion.

K
Karan Nair (India)
Thorough coverage of relational data, node classification, and link prediction

A remarkably clear and well-structured guide. It bridges complex graph theory and machine learning concepts into digestible, actionable chapters.

M
Meera Rao (India)
Transformed how our AI team approaches network data and machine learning

As a Senior AI Engineer, this manual gave me exact model architectures, graph representation workflows, and analytical toolkits needed to build advanced systems with precision.