Author: Pradeep Singh

Brand: McGraw-Hill Education

Binding: paperback

Number Of Pages: 584

Release Date: 31-07-2025

Details: 📘 About the Book / Overview

This book is designed to introduce beginners to the foundational concepts and techniques that underpin AI, focusing on key concepts of Artificial Intelligence from basic to advanced. The aim is to provide a comprehensive guide that bridges theory and practice, empowering readers to harness the power of data to build intelligent solutions.
Structured to facilitate a gradual and in-depth understanding, this book takes readers on a journey through the essential aspects of Artificial Intelligence, Machine Learning, and Data Science, along with important concepts such as Reinforcement Learning, Neural Networks and Deep Learning, Natural Language Processing (NLP), and advanced machine learning techniques.
To enrich the learning experience, practical examples and exercises are included in every chapter. Additionally, future trends in AI are explored, encouraging continuous curiosity and exploration of new advancements in this dynamic field.

✨ Salient Features
✔️ Comprehensive textbook covering Artificial Intelligence (AI) and Machine Learning (ML). ✔️ Aligned with the syllabi of universities across India where AI and ML are combined subjects. ✔️ Emphasis on hands-on learning through code snippets, practical examples, and real-world case studies. ✔️ Inclusion of the latest methodologies, tools, and trends in Data Science and AI. ✔️ Discusses related topics such as NLP, Deep Learning, Neural Networks, Data Mining, and Data Warehousing.

🌐 Web Supplements
Instructor Resources:
A wealth of information is available online through McGraw-Hill’s Connect platform: https://connect.mheducation.com 📊 Lecture PowerPoint Slides 📝 Solution Manual Student Resources:
Available at: www.mheducation.co.in 📂 Data Sets 🧠 Chapter-wise Test Bank ✔️ Answer Keys to Chapter-End Objective Questions 💡 Hints to Chapter-End Numerical Questions 📝 Additional Chapter-Wise Solved Examples 🔗 Links to Reference Materials 📚 Appendices: Linear Algebra; Probability and Statistics; Geometry and Multivariate Calculus

📑 Table of Contents
1️⃣ Foundations of Artificial Intelligence 2️⃣ Intelligent Agents and Environments 3️⃣ Search and Problem Solving 4️⃣ Knowledge Representation and Logical Reasoning 5️⃣ Probabilistic Reasoning and Uncertainty 6️⃣ Machine Learning Basics and Techniques 7️⃣ Supervised Learning Techniques 8️⃣ Unsupervised Learning Techniques 9️⃣ Neural Networks and Deep Learning 🔟 Reinforcement Learning 1️⃣1️⃣ Advanced Machine Learning Techniques 1️⃣2️⃣ Natural Language Processing (NLP) 1️⃣3️⃣ Data Mining and Predictive Analysis 1️⃣4️⃣ Data Warehousing and Business Intelligence 1️⃣5️⃣ Ethics in AI and Future Trends 🔍 Index

EAN: 9789364440820

Package Dimensions: 9.4 x 7.2 x 0.9 inches

Languages: English