Hands-On Automated Machine Learning · A Beginner's Guide to Building Automated Machine Learning Systems Using AutoML and Python by Das, Sibanjan -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Hands-On Automated Machine Learning

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Contributors

About the authors About the reviewers Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images 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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