Scala:Applied Machine Learning
About this ebook
This Learning Path is for engineers and scientists who are familiar with Scala and want to learn how to create, validate, and apply machine learning algorithms. It will also benefit software developers with a background in Scala programming who want to apply machine learning.
What You Will LearnCreate Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizationsDeploy scalable parallel applications using Apache Spark, loading data from HDFS or HiveSolve big data problems with Scala parallel collections, Akka actors, and Apache Spark clustersApply key learning strategies to perform technical analysis of financial marketsUnderstand the principles of supervised and unsupervised learning in machine learningWork with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquetConstruct reliable and robust data pipelines and manage data in a data-driven enterpriseImplement scalable model monitoring and alerts with ScalaIn DetailThis Learning Path aims to put the entire world of machine learning with Scala in front of you.
Scala for Data Science, the first module in this course, 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.
The second course, 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.
The next module, 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.
By the end of this course, you will be a master at Scala machine learning and have enough expertise to be able to build complex machine learning projects using Scala.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
Scala for Data Science, Pascal BugnionScala for Machine Learning, Patrick NicolasMastering Scala Machine Learning, Alex KozlovStyle and approachA tutorial with complete examples, this course will give you the tools to start building useful data engineering and data science solutions straightaway. This course provides practical examples from the field on how to correctly tackle data analysis problems, particularly for modern Big Data datasets.
About the author
Pascal Bugnion is a data engineer at the ASI, a consultancy offering bespoke data science services. Previously, he was the head of data engineering at SCL Elections. He holds a PhD in computational physics from Cambridge University.Patrick Nicolas is a lead R&D engineer at Dell in Santa Clara, California. Patrick has 25 years of experience in software engineering, building large scala applications in C++, Java and Scala, including several managerial positions.Alex Kozlov is a multidisciplinary big data scientist. He came to Silicon Valley in 1991, got his Ph.D. from Stanford University under the supervision of Prof. Daphne Koller and Prof. John Hennessy in 1998, and has been around a few computer and data management companies since..
Patrick R. Nicolas is a lead R&D engineer at Dell in Santa Clara, California. He has 25 years of experience in software engineering and building large-scale applications in C++, Java, and Scala, and has held several managerial positions. His interests include real-time analytics, modeling, and optimization.
Alex Kozlov is a multidisciplinary big data scientist. He came to Silicon Valley in 1991, got his Ph.D. from Stanford University under the supervision of Prof. Daphne Koller and Prof. John Hennessy in 1998, and has been around a few computer and data management companies since. His latest stint was with Cloudera, the leader in Hadoop, where he was one of the early employees and ended up heading the solution architects group on the West Coast. Before that, he spent time with an online advertising company, Turn, Inc.; and before that, he had the privilege to work with HP Labs researchers at HP Inc., and on data mining software at SGI, Inc. Currently, Alexander is the chief solutions architect at an enterprise security startup, E8 Security, where he came to understand the intricacies of catching bad guys in the Internet universe. On the non-professional side, Alexander lives in Sunnyvale, CA, together with his beautiful wife, Oxana, and other important family members, including three daughters, Lana, Nika, and Anna, and a cat and dog. His family also included a hamster and a fih at one point. Alex is an active participant in Silicon Valley technology groups and meetups, and although he is not an offiial committer of any open source projects, he defiitely contributed to many of them in the form of code or discussions. Alexander is an active coder and publishes his open source code at https://github.com/alexvk. Other information can be looked up on his LinkedIn page at https://www.linkedin.com/in/alexvk
