Machine Learning and Deep Learning Using Python and TensorFlow /

This book provides you with an in-depth treatment of some advanced machine learning methods such as random forests, boosting, and neural networks.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Kadre, Shailendra (Autor), Reddy Konasani, Venkata (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, N.Y. : McGraw-Hill Education, [2021]
Edición:First edition.
Colección:McGraw-Hill's AccessEngineering.
Temas:
Machine learning.
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Dedication
  • About the Authors
  • Contents
  • Acknowledgments
  • Preface
  • Chapter 1. Introduction to Machine Learning and Deep Learning
  • 1.1 A Brief History of AI and Machine Learning
  • 1.2 Building Blocks of a Machine Learning Project
  • 1.3 Machine Learning Algorithms vs. Traditional Computer Programs
  • 1.4 How Deep Learning Works
  • 1.5 Machine Learning and Deep Learning Applications
  • 1.6 The Organization of This Book
  • 1.7 Prerequisites?Essential Mathematics
  • 1.8 The Terminology You Should Know
  • 1.9 Machine Learning?A Wider Outlook Will Certainly Help
  • 1.10 Python and Its Potential as the Language of Machine Learning
  • 1.11 About TensorFlow
  • 1.12 Conclusion
  • 1.13 References
  • Chapter 2. Basics of Python Programming and Statistics
  • 2.1 Introduction to Python
  • 2.2 Getting Started with Python Coding
  • 2.3 Types of Objects in Python
  • 2.4 Python Packages
  • 2.5 Conditions and Loops in Python
  • 2.6 Data Handling and Pandas Deep Dive
  • 2.7 Basic Descriptive Statistics
  • 2.8 Data Exploration
  • 2.9 Conclusion
  • 2.10 Practice Problems
  • 2.11 References
  • Chapter 3. Regression and Logistic Regression
  • 3.1 What Is Regression?
  • 3.2 Regression Model Building
  • 3.3 R-Squared
  • 3.4 Multiple Regression
  • 3.5 Multicollinearity in Regression
  • 3.6 Individual Impact of the Variables in Regression
  • 3.7 Steps Needed in Building a Regression Model
  • 3.8 Logistic Regression Model
  • 3.9 Logistic Regression Model Building
  • 3.10 Accuracy of Logistic Regression Line
  • 3.11 Multiple Logistic Regression Line
  • 3.12 Multicollinearity in Logistic Regression
  • 3.13 Individual Impact of the Variables
  • 3.14 Steps in Building a Logistic Regression Model
  • 3.15 Linear vs. Logistic Regression Comparison
  • 3.16 Conclusion
  • 3.17 Practice Problems
  • 3.18 Reference
  • Chapter 4. Decision Trees
  • 4.1 What Are Decision Trees?
  • 4.2 Splitting Criterion Metrics: Entropy and Information Gain
  • 4.3 Decision Tree Algorithm
  • 4.4 Case Study: Contact Center Customer Segmentation
  • 4.5 The Problem of Overfitting
  • 4.6 Pruning of Decision Trees
  • 4.7 The Challenge of Underfitting
  • 4.8 Binary Search on Pruning Parameters
  • 4.9 More Pruning Parameters
  • 4.10 Steps in Building a Decision Tree Model
  • 4.11 Conclusion
  • 4.12 Practice Problems
  • Chapter 5. Model Selection and Cross-Validation
  • 5.1 Steps in Building a Model
  • 5.2 Model Validation Measures: Regression
  • 5.3 Case Study: House Sales in King County, Washington
  • 5.4 Model Validation Measures: Classification
  • 5.5 Bias-Variance Trade-Off
  • 5.6 Cross-Validation
  • 5.7 Feature Engineering Tips and Tricks
  • 5.8 Dealing with Class Imbalance
  • 5.9 Conclusion
  • 5.10 Practice Problems
  • 5.11 References
  • Chapter 6. Cluster Analysis
  • 6.1 Unsupervised Learning
  • 6.2 Distance Measure
  • 6.3 K-Means Clustering Algorithm
  • 6.4 Building K-Means Clusters
  • 6.5 Deciding the Number of Clusters
  • 6.6 Conclusion
  • 6.7 Practice Problems
  • 6.8 References
  • Chapter 7. Random Forests and Boosting
  • 7.1 Ensemble Models
  • 7.2 Bagging
  • 7.3 Random Forest
  • 7.4 Case Study: Car Accidents Prediction
  • 7.5 Boosting
  • 7.6 AdaBoosting Algorithm
  • 7.7 Gradient Boosting Algorithm
  • 7.8 Case Study: Income Prediction from Census Data
  • 7.9 Conclusion
  • 7.10 Practice Problems
  • 7.11 References
  • Chapter 8. Artificial Neural Networks
  • 8.1 Network Diagram for Logistic Regression
  • 8.2 Concept of Decision Boundary
  • 8.3 Multiple Decision Boundaries Problem
  • 8.4 Multiple Decision Boundaries Solution
  • 8.5 Neural Network Intuition
  • 8.6 Neural Network Algorithm
  • 8.7 The Concept of Gradient Descent
  • 8.8 Case Study: Recognizing Handwritten Digits
  • 8.9 Deep Neural Networks
  • 8.10 Conclusion
  • 8.11 Practice Problems
  • 8.12 References
  • Chapter 9. TensorFlow and Keras
  • 9.1 Deep Neural Networks
  • 9.2 Deep Learning Frameworks
  • 9.3 Key Terms in TensorFlow
  • 9.4 Model Building with TensorFlow
  • 9.5 Keras
  • 9.6 Conclusion
  • 9.7 References
  • Chapter 10. Deep Learning Hyperparameters
  • 10.1 Regularization
  • 10.2 Dropout Regularization
  • 10.3 Early Stopping Method
  • 10.4 Loss Functions
  • 10.5 Activation Functions
  • 10.6 Learning Rate
  • 10.7 Optimizers
  • 10.8 Conclusion
  • Chapter 11. Convolutional Neural Networks
  • 11.1 ANNs for Images
  • 11.2 Filters
  • 11.3 The Convolution Layer
  • 11.4 Pooling Layer
  • 11.5 CNN Architecture
  • 11.6 Case Study: Sign Language Reading from Images
  • 11.7 Scheming the Ideal CNN Architecture
  • 11.8 Steps in Building a CNN Model
  • 11.9 Conclusion
  • 11.10 Practice Problems
  • 11.11 References
  • Chapter 12. Recurrent Neural Networks and Long Short-Term Memory
  • 12.1 Cross-Sectional Data vs. Sequential Data
  • 12.2 Models for Sequential Data
  • 12.3 Case Study: Word Prediction
  • 12.4 Recurrent Neural Networks
  • 12.5 RNN for Long Sequences
  • 12.6 Long Short-Term Memory
  • 12.7 Sequence to Sequence Models
  • 12.8 Case Study: Language Translation
  • 12.9 Conclusion
  • 12.10 Practice Problems
  • 12.11 References
  • Index.

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