Ultimate Parallel and Distributed Computing with Julia For Data Science

Unleash Julia’s power: Code Your Data Stories, Shape Machine Intelligence!

KEY FEATURES 
● Comprehensive Learning Journey from fundamentals of Julia ML to advanced techniques.
● Immersive practical approach with real-world examples, exercises, and scenarios, ensuring immediate application of acquired knowledge.
● Delve into the unique features of Julia and unlock its true potential to excel in modern ML applications.

DESCRIPTION
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.

WHAT WILL YOU LEARN 
●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.

WHO IS THIS BOOK FOR?
This book is designed to be a comprehensive and accessible companion for anyone eager to excel in machine learning and data analysis using Julia. Whether you are a novice or an experienced practitioner, the knowledge and skills imparted within these pages will empower you to navigate the complexities of modern data science with Julia.

1. Julia In Data Science Arena
2. Getting Started with Julia
3. Features Assisting Scaling ML Projects
4. Data Structures in Julia
5. Working With Datasets In Julia
6. Basics of Statistics
7. Probability Data Distributions
8. Framing Data in Julia
9. Working on Data in DataFrames
10. Visualizing Data in Julia
11. Introducing Machine Learning in Julia
12. Data and Models
13. Bayesian Statistics and Modeling
14. Parallel Computation in Julia
15. Distributed Computation in Julia
      Index

Nabanita Dash, a results-oriented Research Engineer, holds a BTech in Computer Science and Engineering from IIIT, India. A former Head of the Programming Club, she blends technology passion with leadership. With a foundation in Mathematics, Physics, Chemistry, and English, she excels in her role as a Research Engineer at Antimodular Research in Montreal. Specializing in deep learning artworks and 2D/3D data analysis, Nabanita previously volunteered for MLPack and OpenMined, contributing to C++ data management and privacy research. Her career journey includes a stint as a Full Stack Developer at Julia Computing. Proficient in ML, DL, Computer Vision, and various frameworks, Nabanita is a dynamic professional in the research and tech domain.

ABOUT TECHNICAL REVIEWERS 

Chayan Datta is a versatile Software Engineer with nearly 5 years of experience across healthcare, Ed-tech, FinTech, and scientific computing. He excels as a full-stack developer creating robust applications, blending technologies like Python, Django, Vue.js, JavaScript, Julia, and Go. His leadership is evident in his past roles, contributing significantly to the success of various platforms. Chayan is deeply involved in DevOps, system design, and scalability. His expertise lies not only in building robust applications but also in orchestrating seamless integration between development and operations. With a keen eye for system design, Chayan envisions and implements architectures that prioritize efficiency, reliability, and scalability. His practices ensure smooth collaboration between development and IT operations, fostering a streamlined and agile development pipeline. His commitment to scalability extends beyond individual projects, encompassing a holistic approach to designing systems that can adapt and grow with evolving business needs.

Adhering to coding practices, he ensures project longevity. Understanding front-end and back-end synergy, he crafts user-friendly applications. He understands the crucial interplay between front-end design and back-end functionality, ensuring that the user interface is not only visually engaging but also seamlessly integrated with the underlying system architecture. His holistic approach to software development encompasses a keen awareness of the end-user experience, resulting in applications that not only meet technical requirements but also delight users with their usability and design aesthetics. Besides work, Chayan contributes to open-source projects and maintains a technical blog, showcasing his commitment to knowledge-sharing and community engagement. He brings a well-rounded perspective to his work, fueled by his avid reading habits and a constant desire to stay updated on the latest industry trends and technologies.

Venkateshprasad Bhat is an electronics engineer and an avid Julia developer. He specializes in developing modeling and simulation tools, along with improving package tooling in Julia. He frequently contributes to open-source software. Additionally, he enjoys building robots, especially working on computer vision and electronic circuitry.