Dash Nabanita. Ultimate Parallel and Distributed Computing with Julia For Data Science: Excel in Data Analysis, Statistical Modeling and Machine Learning by ... to optimize workflows

Orange Education Pvt Ltd., 2024. — 484 p. — ISBN: 13: 978-93-91246-86-0.Unleash Julia’s power: Code Your Data Stories, Shape Machine Intelligence!Book DescriptionThis book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll implement algorithms confidently using MLJ.jl and MLBase.jl, paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using Turing.jl, enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as Plots.jl for visualizing data and results.The handbook culminates in optimizing workflows with Julia's parallel and distributed computing capabilities, ensuring efficient and scalable data processing using Distributions.jl, Distributed.jl, and SharedArrays.jl. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning.Julia In Data Science Arena.
Getting Started with Julia.
Features Assisting Scaling ML Projects.
Data Structures in Julia.
Working With Datasets In Julia.
Basics of Statistics.
Probability Data Distributions.
Framing Data in Julia.
Working on Data in DataFrames.
Visualizing Data in Julia.
Introducing Machine Learning in Julia.
Data and Models.
Bayesian Statistics and Modeling.
Parallel Computation in Julia.
Distributed Computation in Julia.