Hands-On Automated Machine Learning电子书

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Hands-On Automated Machine Learning

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Contributors

About the authors

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Preface

Who this book is for

What this book covers

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Conventions used

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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)

Summary

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 variance

Univariate feature selection

Recursive feature elimination

Feature selection using random forest

Feature selection using dimensionality reduction

Principal Component Analysis

Feature generation

Summary

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

Linearity versus non-linearity

Drawing decision boundaries

Decision boundary of logistic regression

The decision boundary of random forest

Commonly used machine learning algorithms

Necessary feature transformations

Supervised ML

Default configuration of auto-sklearn

Finding the best ML pipeline for product line prediction

Finding the best machine learning pipeline for network anomaly detection

Unsupervised AutoML

Commonly used clustering algorithms

Creating sample datasets with sklearn

K-means algorithm in action

The DBSCAN algorithm in action

Agglomerative clustering algorithm in action

Simple automation of unsupervised learning

Visualizing high-dimensional datasets

Principal Component Analysis in action

t-SNE in action

Adding simple components together to improve the pipeline

Summary

Hyperparameter Optimization

Technical requirements

Hyperparameters

Warm start

Bayesian-based hyperparameter tuning

An example system

Summary

Creating AutoML Pipelines

Technical requirements

An introduction to machine learning pipelines

A simple pipeline

FunctionTransformer

A complex pipeline

Summary

Dive into Deep Learning

Technical requirements

Overview of neural networks

Neuron

Activation functions

The step function

The sigmoid function

The ReLU function

The tanh function

A feed-forward neural network using Keras

Autoencoders

Convolutional Neural Networks

Why CNN?

What is convolution?

What are filters?

The convolution layer

The ReLU layer

The pooling layer

The fully connected layer

Summary

Critical Aspects of ML and Data Science Projects

Machine learning as a search

Trade-offs in machine learning

Engagement model for a typical data science project

The phases of an engagement model

Business understanding

Data understanding

Data preparation

Modeling

Evaluation

Deployment

Summary

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