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Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R /
Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop t...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Berkeley] :
Apress,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Basics of Machine Learning; Regression and Classification; Training and Testing Data; The Need for Validation Dataset; Measures of Accuracy; Absolute Error; Root Mean Square Error; Confusion Matrix; AUC Value and ROC Curve; Unsupervised Learning; Typical Approach Towards Building a Model; Where Is the Data Fetched From?; Which Data Needs to Be Fetched?; Pre-processing the Data; Feature Interaction; Feature Generation; Building the Models; Productionalizing the Models.
- Build, Deploy, Test, and Iterate; Summary; Chapter 2: Linear Regression; Introducing Linear Regression; Variables: Dependent and Independent; Correlation; Causation; Simple vs. Multivariate Linear Regression; Formalizing Simple Linear Regression; The Bias Term; The Slope; Solving a Simple Linear Regression; More General Way of Solving a Simple Linear Regression; Minimizing the Overall Sum of Squared Error; Solving the Formula; Working Details of Simple Linear Regression; Complicating Simple Linear Regression a Little; Arriving at Optimal Coefficient Values; Introducing Root Mean Squared Error.
- Running a Simple Linear Regression in R; Residuals; Coefficients; SSE of Residuals (Residual Deviance); Null Deviance; R Squared; F-statistic; Running a Simple Linear Regression in Python; Common Pitfalls of Simple Linear Regression; Multivariate Linear Regression; Working details of Multivariate Linear Regression; Multivariate Linear Regression in R; Multivariate Linear Regression in Python; Issue of Having a Non-significant Variable in the Model; Issue of Multicollinearity; Mathematical Intuition of Multicollinearity; Further Points to Consider in Multivariate Linear Regression.
- Assumptions of Linear Regression; Summary; Chapter 3: Logistic Regression; Why Does Linear Regression Fail for Discrete Outcomes?; A More General Solution: Sigmoid Curve; Formalizing the Sigmoid Curve (Sigmoid Activation); From Sigmoid Curve to Logistic Regression; Interpreting the Logistic Regression; Working Details of Logistic Regression; Estimating Error; Scenario 1; Scenario 2; Least Squares Method and Assumption of Linearity; Running a Logistic Regression in R; Running a Logistic Regression in Python; Identifying the Measure of Interest; Common Pitfalls.
- Time Between Prediction and the Event Happening; Outliers in Independent variables; Summary; Chapter 4: Decision Tree; Components of a Decision Tree; Classification Decision Tree When There Are Multiple Discrete Independent Variables; Information Gain; Calculating Uncertainty: Entropy; Calculating Information Gain; Uncertainty in the Original Dataset; Measuring the Improvement in Uncertainty; Which Distinct Values Go to the Left and Right Nodes; Gini Impurity; Splitting Sub-nodes Further; When Does the Splitting Process Stop?; Classification Decision Tree for Continuous Independent Variables.