Petricek T. Analyzing and Visualizing Data with F#

O’Reilly Media, 2016This report explains many of the key features of the F# language that make it a great tool for data science and machine learning. Real world examples take you through the entire data science workflow with F#:
- How F# and type providers ease the chore of data access
- The process of data analysis and visualization, using the Deedle library, R type provider and the XPlot charting library
- Implementing a clustering algorithm and how the F# type inference helps you understand your codeThe report also includes a list of resources to help you learn more about using F# for data science.Chapter 1: Accessing Data with Type Providers
The examples in this chapter focus on the access part of the data science workflow. In most languages, this is typically the most frustrating part of the access, analyze, visualize loop. In F#, type providers come to the rescue!Chapter 2: Analyzing Data using F# and Deedle
In this chapter, we look at a more realistic case study of doing data science with F#. We use World Bank as our data source, but this time we call it directly using the XML provider. This demonstrates a general approach that works with any REST-based service.Chapter 3: Implementing Machine Learning Algorithms
This chapter completes our brief tour by using the F# language to implement the k-means clustering algorithm. This illustrates two aspects of F# that make it nice for writing algorithms: type inference and interactive development style.Chapter 4: Conclusions and Next Steps
If you want to learn more about using F# for data science and machine learning, there are a number of excellent resources that are worth checking out now that you have finished the quick overview from this report. This chapter gives you a good list!Страница книги:
https://fslab.org/report/Код:
https://github.com/fslaborg/OReilly.Report/