Machine Learning Algorithms Using Scikit and TensorFlow Environments

✍ Scribed by Puvvadi Baby Maruthi, Smrity Prasad, Amit Kumar Tyagi

• Year: 2023
• Tongue: English
• Leaves: 473
• Series: premier reference source
• Category: Library

✦ Synopsis

Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.

✦ Table of Contents

Title Page
Copyright Page
Book Series
Editorial Advisory Board
Table of Contents
Detailed Table of Contents
Preface
Acknowledgment
Chapter 1: Classification Models in Machine Learning Techniques
Chapter 2: Machine Learning Algorithm With TensorFlow and SciKit for Next Generation Systems
Chapter 3: Understanding Convolutional Neural Network With TensorFlow
Chapter 4: A Deep Understanding of Long Short-Term Memory for Solving Vanishing Error Problem
Chapter 5: Coffee Leaf Diseases Classification Using Deep Learning Approach
Chapter 6: COVID-19 Classification With Healthcare Images Based on ML-DL Methods
Chapter 7: Unravelling the Enigma of Machine Learning Model Interpretability in Enhancing Disease Prediction
Chapter 8: Deep Learning for the Intersection of Ethics and Privacy in Healthcare
Chapter 9: Early Detection of Alzheimer's Using Artificial Intelligence for Effective Emotional Support Systems
Chapter 10: Malware Analysis and Classification Using Machine Learning Models
Chapter 11: Improved Breast Cancer Detection in Mammography Images
Chapter 12: Predicting Depression From Social Media Users by Using Lexicons and Machine Learning Algorithms
Chapter 13: Mental Stress Detection Using Bidirectional Encoder Representations From Transformers
Chapter 14: SCRNN
Chapter 15: SRAM Memory Testing Methods and Analysis
Chapter 16: Imagining the Sustainable Future With Industry 6.0
Chapter 17: Dew Computing
Chapter 18: The Future of Artificial Intelligence in Blockchain Applications
Chapter 19: Transformative Effects of ChatGPT on the Modern Era of Education and Society
Chapter 20: Using Ensemble Learning and Random Forest Techniques to Solve Complex Problems
Compilation of References
About the Contributors
Index