Ultimate Parallel and Distributed Computing with Julia For Data Science

<p><strong style=color: rgba(0 0 0 1)>Unleash Julia's power: Code Your Data Stories Shape Machine Intelligence!</strong></p><p><br></p><p><strong style=color: rgba(0 0 0 1)>Book Description</strong></p><p><span style=color: rgba(0 0 0 1)>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.</span></p><p><br></p><p><span style=color: rgba(0 0 0 1)>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.</span></p><p><br></p><p><strong style=color: rgba(0 0 0 1)>Table of Contents</strong></p><p><span style=color: rgba(0 0 0 1)>1. Julia In Data Science Arena</span></p><p><span style=color: rgba(0 0 0 1)>2. Getting Started with Julia</span></p><p><span style=color: rgba(0 0 0 1)>3. Features Assisting Scaling ML Projects</span></p><p><span style=color: rgba(0 0 0 1)>4. Data Structures in Julia</span></p><p><span style=color: rgba(0 0 0 1)>5. Working With Datasets In Julia</span></p><p><span style=color: rgba(0 0 0 1)>6. Basics of Statistics</span></p><p><span style=color: rgba(0 0 0 1)>7. Probability Data Distributions</span></p><p><span style=color: rgba(0 0 0 1)>8. Framing Data in Julia</span></p><p><span style=color: rgba(0 0 0 1)>9. Working on Data in DataFrames</span></p><p><span style=color: rgba(0 0 0 1)>10. Visualizing Data in Julia</span></p><p><span style=color: rgba(0 0 0 1)>11. Introducing Machine Learning in Julia</span></p><p><span style=color: rgba(0 0 0 1)>12. Data and Models</span></p><p><span style=color: rgba(0 0 0 1)>13. Bayesian Statistics and Modeling</span></p><p><span style=color: rgba(0 0 0 1)>14. Parallel Computation in Julia</span></p><p><span style=color: rgba(0 0 0 1)>15. Distributed Computation in Julia</span></p><p><span style=color: rgba(0 0 0 1)>      </span><strong style=color: rgba(0 0 0 1)>Index</strong></p>