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Full Description
Data Analytics using Machine Learning techniques on Cloud Platforms examines how machine learning (ML) and cloud computing combine to drive data-driven decision-making across industries. Covering ML techniques, loud-based analytics tools and security concerns, this book provides theoretical foundations and real-world applications in fields like healthcare, logistics and e-commerce. It also addresses security challenges, privacy concerns and compliance frameworks, ensuring a comprehensive understanding of cloud-based analytics.
This book:
• Covers supervised and unsupervised learning, including regression, clustering, classification and neural networks.
• Discusses Hadoop, Spark, Tableau, Power BI and Splunk for analytics and visualization.
• Examines how cloud computing enhances scalability, efficiency and automation in data analytics.
• Showcases ML-driven solutions in e-commerce, supply chain logistics, healthcare and education.
This book is an essential resource for students, researchers and professionals who seek to understand and apply ML-driven cloud analytics in real-world scenarios.
Contents
Preface
Author biography
Introduction
1. Data Analytics: An Overview 2. Data Analytics: Tools and Technologies 3. Data Analytics: Statistical Approach 4. Supervised and Unsupervised Methods of Machine Learning used in Data Analytics 5. Opportunities and Challenges for Data Analytics Integrated with Machine Learning 6. Cloud Computing: A Change in the IT Infrastructure Landscape 7. Redefining Data Analytics with Machine Learning and Cloud 8. Data Analytics and Cloud together: A powerful combination for E-commerce and Supply Chain logistics 9. Data Analytics, Machine Learning, and Cloud Together: A Powerful Combination for Healthcare & Education 10. Security and Privacy issues for data analytics using machine learning in cloud computing 11. Future Trends for ML-Based Data Analytics in the Cloud
