Tabla de Contenidos: “…-- Understanding Odds -- Logit Function -- Sigmoid Curve -- Using the Breast Cancer Wisconsin (Diagnostic) Data Set -- Examining the Relationship Between Features -- Plotting the Features in 2D -- Plotting in 3D -- Training Using One Feature -- Finding the Intercept and Coefficient -- Plotting the Sigmoid Curve -- Making Predictions -- Training the Model Using All Features -- Testing the Model -- Getting the Confusion Matrix -- Computing Accuracy, Recall, Precision, and Other Metrics -- Receiver Operating Characteristic (ROC) Curve -- Plotting the ROC and Finding the Area Under the Curve (AUC) -- Summary -- Chapter 8 Supervised Learning-Classification Using Support Vector Machines -- What Is a Support Vector Machine? …”

Tabla de Contenidos: “…-- Free Access for Packt account holders -- Preface -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Errata -- Piracy -- Questions -- 1. …”

Tabla de Contenidos: “…Algorithms for multi-class classification -- One-vs-all and one-vs-one strategies -- Metrics for multi-class classification problems -- Confusion matrix -- Precision -- Recall -- F1 score -- Macro- and micro-averaged metrics -- Area Under Curve (AUC-ROC) -- Log loss and its application in measuring calibration of multi-class models -- Brier score and its application in measuring the calibration of multi-class models -- How conformal prediction can be applied to multi-class classification problems -- Multi-class probabilistic classification using inductive and cross-Venn-ABERS predictors -- Summary -- Index -- Other Books You May Enjoy -- Blank Page…”

Tabla de Contenidos: “…Chapter 15: Introducing Neural Networks -- Deep learning at a glance -- Artificial neural networks -- MLPs with Keras -- Interfacing Keras to scikit-learn -- Summary -- Chapter 16: Advanced Deep Learning Models -- Deep model layers -- Fully connected layers -- Convolutional layers -- Dropout layers -- Batch normalization layers -- Recurrent Neural Networks -- An example of a deep convolutional network with Keras -- An example of an LSTM network with Keras -- A brief introduction to TensorFlow -- Computing gradients -- Logistic regression -- Classification with a multilayer perceptron -- Image convolution -- Summary -- Chapter 17: Creating a Machine Learning Architecture -- Machine learning architectures -- Data collection -- Normalization and regularization -- Dimensionality reduction -- Data augmentation -- Data conversion -- Modeling/grid search/cross-validation -- Visualization -- GPU support -- A brief introduction to distributed architectures -- Scikit-learn tools for machine learning architectures -- Pipelines -- Feature unions -- Summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…Front Cover -- Data Science -- Copyright Page -- Dedication -- Contents -- Foreword -- Preface -- Why Data Science? -- Why This Book? -- Who Can Use This Book? -- Acknowledgments -- 1 Introduction -- 1.1 AI, Machine learning, and Data Science -- 1.2 What is Data Science? …”