- 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
Scala is considered to be a successor to Java in the area of Big Data by many. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists
This Learning Path aims to put the entire world of machine learning with Scala in front of you. We will begin by introducing you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. Moving on, we'll introduce machine learning in Scala and take a very deep dive into leveraging Scala to construct and study systems that can learn from data. Finally, we will master Scala machine learning in breadth and impart expertise for you to be able to build complex machine learning projects using Scala.
Module 1, Scala for Data Science, is a tutorial guide that provides tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed building data science and data engineering solutions.
Module 2, Scala for Machine Learning, guides you through the process of building AI applications with diagrams, formal mathematical notation, source code snippets, and useful tips. A review of the Akka framework and Apache Spark clusters concludes the tutorial.
Module 3, Mastering Scala Machine Learning, is the final step in this course. It will take your knowledge to next level and help you use the knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees.
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