1. Getting Started
by Alex Kozlov, Patrick R. Nicolas, Pascal Bugnion
Scala:Applied Machine Learning
- Scala:Applied Machine Learning
- Table of Contents
- Scala:Applied Machine Learning
- Scala:Applied Machine Learning
- Credits
- Preface
- I. Module 1
- 1. Scala and Data Science
- 2. Manipulating Data with Breeze
- Code examples
- Installing Breeze
- Getting help on Breeze
- Basic Breeze data types
- Vectors
- Dense and sparse vectors and the vector trait
- Matrices
- Building vectors and matrices
- Advanced indexing and slicing
- Mutating vectors and matrices
- Matrix multiplication, transposition, and the orientation of vectors
- Data preprocessing and feature engineering
- Breeze – function optimization
- Numerical derivatives
- Regularization
- An example – logistic regression
- Towards re-usable code
- Alternatives to Breeze
- Summary
- References
- 3. Plotting with breeze-viz
- 4. Parallel Collections and Futures
- 5. Scala and SQL through JDBC
- Interacting with JDBC
- First steps with JDBC
- JDBC summary
- Functional wrappers for JDBC
- Safer JDBC connections with the loan pattern
- Enriching JDBC statements with the "pimp my library" pattern
- Wrapping result sets in a stream
- Looser coupling with type classes
- Creating a data access layer
- Summary
- References
- 6. Slick – A Functional Interface for SQL
- 7. Web APIs
- 8. Scala and MongoDB
- 9. Concurrency with Akka
- GitHub follower graph
- Actors as people
- Hello world with Akka
- Case classes as messages
- Actor construction
- Anatomy of an actor
- Follower network crawler
- Fetcher actors
- Routing
- Message passing between actors
- Queue control and the pull pattern
- Accessing the sender of a message
- Stateful actors
- Follower network crawler
- Fault tolerance
- Custom supervisor strategies
- Life-cycle hooks
- What we have not talked about
- Summary
- References
- 10. Distributed Batch Processing with Spark
- 11. Spark SQL and DataFrames
- DataFrames – a whirlwind introduction
- Aggregation operations
- Joining DataFrames together
- Custom functions on DataFrames
- DataFrame immutability and persistence
- SQL statements on DataFrames
- Complex data types – arrays, maps, and structs
- Interacting with data sources
- Standalone programs
- Summary
- References
- 12. Distributed Machine Learning with MLlib
- 13. Web APIs with Play
- Client-server applications
- Introduction to web frameworks
- Model-View-Controller architecture
- Single page applications
- Building an application
- The Play framework
- Dynamic routing
- Actions
- Interacting with JSON
- Querying external APIs and consuming JSON
- Creating APIs with Play: a summary
- Rest APIs: best practice
- Summary
- References
- 14. Visualization with D3 and the Play Framework
- A. Pattern Matching and Extractors
- II. Module 2
- 1. Getting Started
- 2. Hello World!
- 3. Data Preprocessing
- 4. Unsupervised Learning
- Clustering
- K-means clustering
- The expectation-maximization algorithm
- Dimension reduction
- Performance considerations
- Summary
- Clustering
- 5. Naïve Bayes Classifiers
- 6. Regression and Regularization
- 7. Sequential Data Models
- 8. Kernel Models and Support Vector Machines
- 9. Artificial Neural Networks
- Feed-forward neural networks
- The multilayer perceptron
- Evaluation
- Convolution neural networks
- Benefits and limitations
- Summary
- 10. Genetic Algorithms
- 11. Reinforcement Learning
- 12. Scalable Frameworks
- A. Basic Concepts
- III. Module 3
- 1. Exploratory Data Analysis
- 2. Data Pipelines and Modeling
- 3. Working with Spark and MLlib
- 4. Supervised and Unsupervised Learning
- 5. Regression and Classification
- 6. Working with Unstructured Data
- 7. Working with Graph Algorithms
- 8. Integrating Scala with R and Python
- 9. NLP in Scala
- 10. Advanced Model Monitoring
- A. Bibliography
- Index
It is critical for any computer scientist to understand the different classes of machine learning algorithms and be able to select the ones that are relevant to the domain of their expertise and dataset. However, the application of these algorithms represents a small fraction of the overall effort needed to extract an accurate and performing model from input data. A common data mining workflow consists of the following sequential steps:
- Defining the problem to solve.
- Loading the data.
- Preprocessing, analyzing, and filtering the input data.
- Discovering patterns, affinities, clusters, and classes, if needed.
- Selecting the model features and appropriate machine learning algorithm(s).
- Refining and validating the model.
- Improving the computational performance of the implementation.
In this book, each stage of the process is critical to build the right model.
Tip
It is impossible to describe the key machine learning algorithms and their implementations in detail in a single book. The sheer quantity of information and Scala code would overwhelm even the most dedicated readers. Each chapter focuses on the mathematics and code that are absolutely essential to the understanding of the topic. Developers are encouraged to browse through the following:
- The Scala coding convention and standard used in the book in the Appendix A, Basic Concepts
- API Scala docs
- A fully documented source code that is available online
This first chapter introduces you to the taxonomy of machine learning algorithms, the tools and frameworks used in the book, and a simple application of logistic regression to get your feet wet.
Each chapter contains a small section dedicated to the formulation of the algorithms for those interested in the mathematical concepts behind the science and art of machine learning. These sections are optional and defined within a tip box. For example, the mathematical expression of the mean and the variance of a variable X mentioned in a tip box will be as follows:
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