Practical Applications of Data Processing, Algorithms, and Modeling by Pawan Whig, Sachinn Sharma, Seema Sharma

Unfortunately, in the contemporary environment focused on data science, there is a huge difference between reading about it and actually implementing data science in practice. Thus, the mission of the upcoming course “Practical Applications of Data Processing, Algorithms, and Modeling” is to assist in overcoming this challenge. Thus, it will be useful for readers who are a novice in data science and for professionals, as well as for those who are just interested in data. It somehow ‘goes deep’ into the matter of data, algorithms, and modeling while keeping it simple and useful for a practitioner.

The book aims to address the main issue in data science: the rift between the theoretical framework and the real-life application of concepts. It also incorporates the modern approaches to data processing, algorithms, and modeling used when defining these concepts in the real world. An approach to real-life solutions helps the reader apply these techniques and procedures in different fields.

As technology evolves with time, this publication provides the reader with new insights and best practices in the field of “Practical Applications of Data Processing, Algorithms, and Modeling.” It contains practical assignments with demonstrations, live illustrations, and realistic problem-solving cases that enhance your comfort in employing data to solve issues and arrive at effective solutions. It’s a beginner’s guide to a familiar world yet not so. Effortlessly and with success, you will make your way through the world of data.

Practical Applications of Data Processing, Algorithms, and Modeling Table of Contents:

1. Chapter 1: Introduction to Data Processing
2. Chapter 2: Machine Learning Mastery
3. Chapter 3: Algorithmic Insights
4. Chapter 4: Architecture, Framework, and Models for Edge-AI in Healthcare
5. Chapter 5: AI-Driven Modeling
6. Chapter 6: Agricultural Insights
7. Chapter 7: A Novel Online Job Scam Detection of Imbalanced Data Using ML and NLP Models
8. Chapter 8: Data Analysis Using IoT Technologies for Enhanced Healthcare Decision-Making
9. Chapter 9: Data Privacy, Compliance, and Security Including AI/ML
10. Chapter 10: Data Privacy, Compliance, and Security in Cloud Computing for Finance
11. Chapter 11: Demystifying Machine Learning by Unraveling Interpretability
12. Chapter 12: Mastering Data Management
13. Chapter 13: Measuring Psychometric Analysis of Stress Levels in Learners Using Machine Learning
14. Chapter 14: Navigating the Essential Fundamentals of Data Processing for Modern Enterprises
15. Chapter 15: Optimizing with Intelligence
16. Chapter 16: Smart Data Processing
17. Chapter 17: Unleashing the Power of Cloud Computing for Data Science
18. Chapter 18: Unveiling the Frontiers
19. Chapter 19: Using Complex Network Analysis Techniques to Uncover Fraudulent Activity in Connected Healthcare Systems
20. Chapter 20: Challenges and Future Directions in Anomaly Detection

Who is this course for?

1. Aspiring data scientists
2. Experienced data professionals
3. Students studying data science
4. Anyone interested in data-driven insights

Click on the links below to Download Practical Applications of Data Processing, Algorithms, and Modeling!