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40 Algorithms Every Data Scientist Should Know

Język publikacji: 1
Autorzy:
Jürgen Weichenberger, Huw Kwon
40 Algorithms Every Data Scientist Should Know Jürgen Weichenberger, Huw Kwon - okladka książki

40 Algorithms Every Data Scientist Should Know Jürgen Weichenberger, Huw Kwon - okladka książki

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Wydawnictwo:
BPB Publications (Z chęcią przeczytam książkę w języku polskim)
Ocena:
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Stron:
588
Dostępne formaty:
     ePub
     Mobi
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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.

This book 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.
Creating intelligent systems and solving real-world problems.
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.

Table of Contents
1. Fundamentals
2. Typical Data Structures
3. 40 AI/ML Algorithms Overview
4. Basic Supervised Learning Algorithms
5. Advanced Supervised Learning Algorithms
6. Basic Unsupervised Learning Algorithms
7. Advanced Unsupervised Learning Algorithms
8. Basic Reinforcement Learning Algorithms
9. Advanced Reinforcement Learning Algorithms
10. Basic Semi-Supervised Learning Algorithms
11. Advanced Semi-Supervised Learning Algorithms
12. Natural Language Processing
13. Computer Vision
14. Large-Scale Algorithms
15. Outlook into the Future: Quantum Machine Learning

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