𝔖 Scriptorium
✦   LIBER   ✦
πŸ“

Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

✍ Scribed by Blaminsky, Jarek; Shukla, Nishant

Publisher
Packt Publishing - ebooks Account
Year
2014
Tongue
English
Leaves
334
Series
Quick answers to common problems
Category
Library
⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis

Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

About This Book

  • A practical and concise guide to using Haskell when getting to grips with data analysis
  • Recipes for every stage of data analysis, from collection to visualization
  • In-depth examples demonstrating various tools, solutions and techniques

Who This Book Is For

This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A good understanding of data sets and functional programming is assumed.

What You Will Learn

  • Obtain and analyze raw data from various sources including text files, CSV files, databases, and websites
  • Implement practical tree and graph algorithms on various datasets
  • Apply statistical methods such as moving average and linear regression to understand patterns
  • Fiddle with parallel and concurrent code to speed up and simplify time-consuming algorithms
  • Find clusters in data using some of the most popular machine learning algorithms
  • Manage results by visualizing or exporting data

In Detail

This book will take you on a voyage through all the steps involved in data analysis. It provides synergy between Haskell and data modeling, consisting of carefully chosen examples featuring some of the most popular machine learning techniques.

You will begin with how to obtain and clean data from various sources. You will then learn how to use various data structures such as trees and graphs. The meat of data analysis occurs in the topics involving statistical techniques, parallelism, concurrency, and machine learning algorithms, along with various examples of visualizing and exporting results. By the end of the book, you will be empowered with techniques to maximize your potential when using Haskell for data analysis.

✦ Subjects

Haskell (Computer program language);COMPUTERS -- Programming Languages -- General.

πŸ“œ SIMILAR VOLUMES

Scala Data Analysis Cookbook: Navigate t
πŸ“ Scala Data Analysis Cookbook: Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes
✍ Arun Manivannan πŸ“‚ Library πŸ“… 2015 πŸ› Packt Publishing 🌐 English

This book will introduce you to the most popular Scala tools, libraries, and frameworks through practical recipes around loading, manipulating, and preparing your data. It will also help you explore and make sense of your data using stunning and insightfulvisualizations, and machine learning toolkit

Clojure Data Analysis Cookbook: Over 110
πŸ“ Clojure Data Analysis Cookbook: Over 110 recipes to help you dive into the world of practical data analysis using Clojure
✍ Eric Rochester πŸ“‚ Library πŸ“… 2013 πŸ› Packt Publishing 🌐 English

Data is everywhere and it's increasingly important to be able to gain insights that we can act on. Using Clojure for data analysis and collection, this book will show you how to gain fresh insights and perspectives from your data with an essential collection of practical, structured recipes. The Cl

Practical Machine Learning for Data Anal
πŸ“ Practical Machine Learning for Data Analysis Using Python
✍ Abdulhamit Subasi πŸ“‚ Library πŸ“… 2020 πŸ› Academic Press 🌐 English

<p><i>Practical Machine Learning for Data Analysis Using Python</i> is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to unders

Ensemble Machine Learning Cookbook: Over
πŸ“ Ensemble Machine Learning Cookbook: Over 35 practical recipes to explore ensemble machine learning techniques using Python
✍ Dipayan Sarkar, Vijayalakshmi Natarajan πŸ“‚ Library πŸ“… 2019 πŸ› Packt Publishing 🌐 English

<p><b>Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more </b></p> <h4>Key Features</h4> <ul><li>Apply popular machine learning algorithms using a recipe-based approach </li> <li>Implement boosting, bagging, and stacking ensemble methods to