Machine Learning Algorithms Using Scikit and TensorFlow Environments

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. Title Page 1 Copyright Page 2 Book Series 3 Editorial Advisory Board 5 Table of Contents 7 Detailed Table of Contents 11 Preface 19 Acknowledgment 20 Chapter 1: Classification Models in Machine Learning Techniques 21 Chapter 2: Machine Learning Algorithm With TensorFlow and SciKit for Next Generation Systems 37 Chapter 3: Understanding Convolutional Neural Network With TensorFlow 70 Chapter 4: A Deep Understanding of Long Short-Term Memory for Solving Vanishing Error Problem 94 Chapter 5: Coffee Leaf Diseases Classification Using Deep Learning Approach 111 Chapter 6: COVID-19 Classification With Healthcare Images Based on ML-DL Methods 132 Chapter 7: Unravelling the Enigma of Machine Learning Model Interpretability in Enhancing Disease Prediction 145 Chapter 8: Deep Learning for the Intersection of Ethics and Privacy in Healthcare 174 Chapter 9: Early Detection of Alzheimer's Using Artificial Intelligence for Effective Emotional Support Systems 212 Chapter 10: Malware Analysis and Classification Using Machine Learning Models 229 Chapter 11: Improved Breast Cancer Detection in Mammography Images 241 Chapter 12: Predicting Depression From Social Media Users by Using Lexicons and Machine Learning Algorithms 269 Chapter 13: Mental Stress Detection Using Bidirectional Encoder Representations From Transformers 286 Chapter 14: SCRNN 296 Chapter 15: SRAM Memory Testing Methods and Analysis 315 Chapter 16: Imagining the Sustainable Future With Industry 6.0 338 Chapter 17: Dew Computing 352 Chapter 18: The Future of Artificial Intelligence in Blockchain Applications 366 Chapter 19: Transformative Effects of ChatGPT on the Modern Era of Education and Society 394 Chapter 20: Using Ensemble Learning and Random Forest Techniques to Solve Complex Problems 408 Compilation of References 428 About the Contributors 464 Index 470