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Total Size:
8.6 MB
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E0CCC19EBDF26E731765AB9FF76603E0FD4385AB
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April 23, 2026, 4:59 a.m.
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(Last updated: April 23, 2026, 5 a.m.)
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| Code.zip | 1.4 MB |
| Images.zip | 1.9 MB |
| Weichenberger J. 40 Algorithms Every Data Scientist Should Know...2025.pdf | 5.3 MB |
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SOURCE: Weichenberger J. 40 Algorithms Every Data Scientist Should Know...2025
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MEDIAINFO
Textbook in PDF format 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 Fundamentals Typical Data Structures 40 AI/ML Algorithms Overview Basic Supervised Learning Algorithms Advanced Supervised Learning Algorithms Basic Unsupervised Learning Algorithms Advanced Unsupervised Learning Algorithms Basic Reinforcement Learning Algorithms Advanced Reinforcement Learning Algorithms Basic Semi-Supervised Learning Algorithms Advanced Semi-Supervised Learning Algorithms Natural Language Processing Computer Vision Large-Scale Algorithms Outlook into the Future: Quantum Machine Learning
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