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Hands-On Automated Machine Learning : a beginner's guide to building automated machine learning systems using AutoML and Python.
This book helps machine learning professionals in developing AutoML systems that can be utilized to build ML solutions. This book covers the necessary foundations and shows the most practical ways possible to get to speed with regards to creating AutoML modules.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to AutoML; Scope of machine learning; What is AutoML?; Why use AutoML and how does it help?; When do you automate ML?; What will you learn?; Core components of AutoML systems; Automated feature preprocessing; Automated algorithm selection; Hyperparameter optimization; Building prototype subsystems for each component; Putting it all together as an end-to-end AutoML system; Overview of AutoML libraries; Featuretools; Auto-sklearn; MLBox; TPOT; Summary.
- Chapter 2: Introduction to Machine Learning Using PythonTechnical requirements; Machine learning; Machine learning process; Supervised learning; Unsupervised learning; Linear regression; What is linear regression?; Working of OLS regression; Assumptions of OLS; Where is linear regression used?; By which method can linear regression be implemented?; Important evaluation metrics
- regression algorithms; Logistic regression; What is logistic regression?; Where is logistic regression used?; By which method can logistic regression be implemented?
- Important evaluation metrics
- classification algorithmsDecision trees; What are decision trees?; Where are decision trees used?; By which method can decision trees be implemented?; Support Vector Machines; What is SVM?; Where is SVM used?; By which method can SVM be implemented?; k-Nearest Neighbors; What is k-Nearest Neighbors?; Where is KNN used?; By which method can KNN be implemented?; Ensemble methods; What are ensemble models?; Bagging; Boosting; Stacking/blending; Comparing the results of classifiers; Cross-validation; Clustering; What is clustering?; Where is clustering used?
- By which method can clustering be implemented?Hierarchical clustering; Partitioning clustering (KMeans); Summary; Chapter 3: Data Preprocessing; Technical requirements; Data transformation; Numerical data transformation; Scaling; Missing values; Outliers; Detecting and treating univariate outliers; Inter-quartile range; Filtering values; Winsorizing; Trimming; Detecting and treating multivariate outliers; Binning; Log and power transformations; Categorical data transformation; Encoding; Missing values for categorical data transformation; Text preprocessing; Feature selection.
- Excluding features with low varianceUnivariate feature selection; Recursive feature elimination; Feature selection using random forest; Feature selection using dimensionality reduction; Principal Component Analysis; Feature generation; Summary; Chapter 4: Automated Algorithm Selection; Technical requirements; Computational complexity; Big O notation; Differences in training and scoring time; Simple measure of training and scoring time ; Code profiling in Python; Visualizing performance statistics; Implementing k-NN from scratch; Profiling your Python script line by line.