更新时间：2021-06-10 19:30:50
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Contributors
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Preface
Who this book is for
What this book covers
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Applied Machine Learning Quick Start
Machine learning and data science
Solving problems with machine learning
Applied machine learning workflow
Data and problem definition
Measurement scales
Data collection
Finding or observing data
Generating data
Sampling traps
Data preprocessing
Data cleaning
Filling missing values
Remove outliers
Data transformation
Data reduction
Unsupervised learning
Finding similar items
Euclidean distances
Non-Euclidean distances
The curse of dimensionality
Clustering
Supervised learning
Classification
Decision tree learning
Probabilistic classifiers
Kernel methods
Artificial neural networks
Ensemble learning
Evaluating classification
Precision and recall
Roc curves
Regression
Linear regression
Logistic regression
Evaluating regression
Mean squared error
Mean absolute error
Correlation coefficient
Generalization and evaluation
Underfitting and overfitting
Train and test sets
Cross-validation
Leave-one-out validation
Stratification
Summary
Java Libraries and Platforms for Machine Learning
The need for Java
Machine learning libraries
Weka
Java machine learning
Apache Mahout
Apache Spark
Deeplearning4j
MALLET
The Encog Machine Learning Framework
ELKI
MOA
Comparing libraries
Building a machine learning application
Traditional machine learning architecture
Dealing with big data
Big data application architecture
Basic Algorithms - Classification Regression and Clustering
Before you start
Data
Loading data
Feature selection
Learning algorithms
Classifying new data
Evaluation and prediction error metrics
The confusion matrix
Choosing a classification algorithm
Classification using Encog
Classification using massive online analysis
Evaluation
Baseline classifiers
Decision tree
Lazy learning
Active learning
