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40 Algorithms Every Data Scientist Should Know - Weichenberger Jürgen

40 Algorithms Every Data Scientist Should Know - Weichenberger Jürgen

  • Navigating through essential AI and ML algorithms (English Edition)

DESCRIPTION 

Mastering AI and ML algorithms is essential for data scientists. This book covers a wide range of techniques, from supervised and unsupervised learning to deep learning and reinforcement learning. This book is a compass to the most important algorithms that every data scientist should have at their disposal when building a new AI/ML application.

It offers a thorough introduction to AI and ML, covering key concepts, data structures, and various algorithms like linear regression, decision trees, and neural networks. It explores learning techniques like supervised, unsupervised, and semi-supervised learning and applies them to real-world scenarios such as natural language processing and computer vision. With clear explanations, code examples, and detailed descriptions of 40 algorithms, including their mathematical foundations and practical applications, this resource is ideal for both beginners and experienced professionals looking to deepen their understanding of AI and ML.

The final part of the book gives an outlook for more state-of-the-art algorithms that will have the potential to change the world of AI and ML fundamentals.


KEY FEATURES  

Covers a wide range of AI and ML algorithms, from foundational concepts to advanced techniques.

Includes real-world examples and code snippets to illustrate the application of algorithms.

Explains complex topics in a clear and accessible manner, making it suitable for learners of all levels.


WHAT YOU WILL LEARN

Differences between supervised, unsupervised, and reinforcement learning.

Gain expertise in data cleaning, feature engineering, and handling different data formats.

Learn to implement and apply algorithms such as linear regression, decision trees, neural networks, and support vector machines.

Learn to approach AI and ML challenges with a structured and analytical mindset.


WHO THIS BOOK IS FOR

This book is ideal for data scientists, ML engineers, and anyone interested in entering the world of AI.




EAN: 9789355519832
Symbol
133GUK03527KS
Rok wydania
2024
Strony
588
Oprawa
Miekka
Format
19.1x23.5cm
Język
angielski
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Symbol
133GUK03527KS
Kod producenta
9789355519832
Rok wydania
2024
Strony
588
Oprawa
Miekka
Format
19.1x23.5cm
Język
angielski
Autorzy
Weichenberger Jürgen

DESCRIPTION 

Mastering AI and ML algorithms is essential for data scientists. This book covers a wide range of techniques, from supervised and unsupervised learning to deep learning and reinforcement learning. This book is a compass to the most important algorithms that every data scientist should have at their disposal when building a new AI/ML application.

It offers a thorough introduction to AI and ML, covering key concepts, data structures, and various algorithms like linear regression, decision trees, and neural networks. It explores learning techniques like supervised, unsupervised, and semi-supervised learning and applies them to real-world scenarios such as natural language processing and computer vision. With clear explanations, code examples, and detailed descriptions of 40 algorithms, including their mathematical foundations and practical applications, this resource is ideal for both beginners and experienced professionals looking to deepen their understanding of AI and ML.

The final part of the book gives an outlook for more state-of-the-art algorithms that will have the potential to change the world of AI and ML fundamentals.


KEY FEATURES  

Covers a wide range of AI and ML algorithms, from foundational concepts to advanced techniques.

Includes real-world examples and code snippets to illustrate the application of algorithms.

Explains complex topics in a clear and accessible manner, making it suitable for learners of all levels.


WHAT YOU WILL LEARN

Differences between supervised, unsupervised, and reinforcement learning.

Gain expertise in data cleaning, feature engineering, and handling different data formats.

Learn to implement and apply algorithms such as linear regression, decision trees, neural networks, and support vector machines.

Learn to approach AI and ML challenges with a structured and analytical mindset.


WHO THIS BOOK IS FOR

This book is ideal for data scientists, ML engineers, and anyone interested in entering the world of AI.




EAN: 9789355519832
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