This is the repository for Ultimate Parallel and Distributed Computing with Julia For Data Science, published by Orange AVA™
This 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.
● Master Julia ML Basics to gain a deep understanding of Julia's syntax, variables, and functions.
● Efficient Data Handling with Julia arrays and DataFrames for streamlined and insightful analysis.
● Develop expertise in both basic and advanced statistical models for informed decision-making through Statistical Modeling.
● Achieve Machine Learning Proficiency by confidently implementing ML algorithms using MLJ.jl and MLBase.jl.
● Apply Bayesian Inference Skills with Turing.jl for advanced modeling techniques.
● Optimize workflows using Julia's Parallel Processing Capabilities and Distributed Computing for efficient and scalable data processing.