更新时间：2021-07-02 16:33:37
coverpage
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
eBooks discount offers and more
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. Exploratory Data Analysis
Getting started with Scala
Distinct values of a categorical field
Summarization of a numeric field
Basic stratified and consistent sampling
Working with Scala and Spark Notebooks
Basic correlations
Summary
Chapter 2. Data Pipelines and Modeling
Influence diagrams
Sequential trials and dealing with risk
Exploration and exploitation
Unknown unknowns
Basic components of a data-driven system
Optimization and interactivity
Chapter 3. Working with Spark and MLlib
Setting up Spark
Understanding Spark architecture
Applications
ML libraries
Spark performance tuning
Running Hadoop HDFS
Chapter 4. Supervised and Unsupervised Learning
Records and supervised learning
Unsupervised learning
Problem dimensionality
Chapter 5. Regression and Classification
What regression stands for?
Continuous space and metrics
Linear regression
Logistic regression
Regularization
Multivariate regression
Heteroscedasticity
Regression trees
Classification metrics
Multiclass problems
Perceptron
Generalization error and overfitting
Chapter 6. Working with Unstructured Data
Nested data
Other serialization formats
Hive and Impala
Sessionization
Working with traits
Working with pattern matching
Other uses of unstructured data
Probabilistic structures
Projections
Chapter 7. Working with Graph Algorithms
A quick introduction to graphs
SBT
Graph for Scala
GraphX
Chapter 8. Integrating Scala with R and Python
Integrating with R
Integrating with Python
Chapter 9. NLP in Scala
Text analysis pipeline
MLlib algorithms in Spark
Segmentation annotation and chunking
POS tagging
Using word2vec to find word relationships
Chapter 10. Advanced Model Monitoring
System monitoring
Index
