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Unleash Julia's power: Code Your Data Stories, Shape Machine Intelligence!
Book 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 [...] 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 [...] and [...], paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using [...], enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as [...] 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 [...], [...] and [...]. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning.
Table of Contents
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
Book 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 [...] 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 [...] and [...], paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using [...], enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as [...] 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 [...], [...] and [...]. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning.
Table of Contents
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
Unleash Julia's power: Code Your Data Stories, Shape Machine Intelligence!
Book 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 [...] 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 [...] and [...], paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using [...], enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as [...] 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 [...], [...] and [...]. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning.
Table of Contents
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
Book 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 [...] 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 [...] and [...], paving the way for advanced data-driven solutions. Explore the realm of Bayesian inference skills through practical applications using [...], enhancing your ability to extract valuable insights. The book also introduces crucial Julia packages such as [...] 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 [...], [...] and [...]. This comprehensive guide equips you with the knowledge and practical insights needed to excel in the dynamic field of data science and machine learning.
Table of Contents
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
Details
Erscheinungsjahr: | 2024 |
---|---|
Fachbereich: | Datenkommunikation, Netze & Mailboxen |
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
ISBN-13: | 9789391246860 |
ISBN-10: | 9391246869 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Dash, Nabanita |
Hersteller: | Orange Education Pvt Ltd |
Verantwortliche Person für die EU: | Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de |
Maße: | 235 x 191 x 26 mm |
Von/Mit: | Nabanita Dash |
Erscheinungsdatum: | 03.01.2024 |
Gewicht: | 0,899 kg |
Details
Erscheinungsjahr: | 2024 |
---|---|
Fachbereich: | Datenkommunikation, Netze & Mailboxen |
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
ISBN-13: | 9789391246860 |
ISBN-10: | 9391246869 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Dash, Nabanita |
Hersteller: | Orange Education Pvt Ltd |
Verantwortliche Person für die EU: | Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de |
Maße: | 235 x 191 x 26 mm |
Von/Mit: | Nabanita Dash |
Erscheinungsdatum: | 03.01.2024 |
Gewicht: | 0,899 kg |
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