Foundations of Machine Learning and AI by Pradeep Singh
This book builds a single, coherent pathway from linear algebra to probability and statistical learning—the twin pillars behind modern Data Science, AI, and ML. With equal emphasis on geometry (matrices, spectra, projections) and uncertainty (randomness, estimation, generalization), it equips readers to derive algorithms from first principles and implement them robustly at scale. Throughout, geometric pictures (projections, angles, spectra) and probabilistic arguments (risk, concentration, generalization) are developed side-by-side. Each concept is motivated by a real ML use case—denoising with PCA, ill-conditioning in regression, choosing regularization via validation curves, or accelerating large least-squares with sketching.
-
Advances in Next-Generation Networking for Cyber-Physical System: TCP, SDN, and Emerging Technologies
-
Digitalization
-
Innovations in Data Science and Analytics
-
Blockchain Innovations for a Sustainable Circular Economy
-
AI Intervention in Digital and Social Marketing
-
Intelligent Governance in the Big Data Era
-
Computational and Data-Driven Approaches in Pharmaceutical Sciences: From Molecular Modelling to Evidence-Based Therapeutics
-
Data Science in Finance and Accounting
-
Cyber Security
-
Internet of Things for Healthcare Technologies
-
New Paradigm of Industry 4.0
-
Blockchain and Deep Learning
-
Cyber and Digital Forensic Investigations
-
Handbook of Machine Learning Applications for Genomics
-
Fog Data Analytics for IoT Applications
-
The Future of Metaverse in the Virtual Era and Physical World
-
Machine Learning for Adaptive Many-Core Machines - A Practical Approach
-
Big Data and Blockchain for Service Operations Management
-
The Geometry of Intelligence: Foundations of Transformer Networks in Deep Learning
-
Concepts and Methods for a Librarian of the Web
-
Blockchain and its Applications in Industry 4.0
-
Deep Learning Through the Prism of Tensors
-
Internet of Things and Analytics for Agriculture, Volume 3
-
Environmental Modeling Using Satellite Imaging and Dataset Re-processing
-
The Power of Data: Driving Climate Change with Data Science and Artificial Intelligence Innovations
-
Recommender System for Improving Customer Loyalty
-
Evolutionary Decision Trees in Large-Scale Data Mining
-
Supply Chain Performance Evaluation
-
The Autonomous Web
-
Total Journalism
-
Big Data in Information Society and Digital Economy
-
Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach
-
Text Mining
-
Information Retrieval and Natural Language Processing
-
The Science and Politics of Extractive Industries in the Ocean and Beyond
-
Proceedings of Fifth International Conference on Computing, Communications, and Cyber-Security
-
The Geometry of Intelligence: Foundations of Transformer Networks in Deep Learning
-
Deep Learning Through the Prism of Tensors
-
Python for Mathematical Thinking
-
Machine Learning and Computational Intelligence Techniques for Data Engineering
-
A Ghost out of the Bicycle
| SKU | Unavailable |
| ISBN 13 | 9783032303356 |
| ISBN 10 | 3032303354 |
| Title | Foundations of Machine Learning and AI |
| Author | Pradeep Singh |
| Series | Studies In Big Data |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer Nature Switzerland AG |
| Year published | 2026-08-18 |
| Number of pages | 558 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |