更新时间：2021-08-27 18:44:59
封面
Title Page
Copyright and Credits
Hands-On Automated Machine Learning
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Contributors
About the authors
About the reviewers
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
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Conventions used
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Reviews
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
Introduction to Machine Learning Using Python
Technical 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 algorithms
Decision 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)
Data Preprocessing
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
