40 Algorithms Every Data Scientist Should Know

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.

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

Jürgen Weichenberger has been working in Artificial Intelligence and Machine Learning Development for more than 20 years, playing central roles in numerous projects as a technical leader and chief data scientist, delivering projects using numerous different algorithms and even developing entire new algorithms for big companies, including well succeeded projects in Europe, Asia, and North America. Currently, he is a Head of AI Strategy and Innovation at Schneider Electric and a Senior Advisor at P.E.T. Consulting. He is also an accomplished postgraduate completing a degree in Executive General Management and holds three master’s degrees focused on Applied Computer Science, Bioinformatics, and Cybernetics. He has many certifications for various types of Artificial Intelligence Technologies. Furthermore, the author participates as a speaker in international AI Conferences and writes technical articles on AI Technologies, Algorithms and related topics. Based on all his contributions to AI communities worldwide, he was awarded over 20 AI patents.

Huw Kwon is a well-known authority in AI and ML within the management consulting world, bringing over two decades of academic rigor and truly extensive practical experience to the field as a first-hand practitioner. With a strong foundation in management science and advanced computational algorithms, his work is unified by a singular focus: leveraging AI to create tangible, sustained, and lasting real- world impact.

Huw’s career spans senior roles in banking and consulting at some of the world’s most prestigious firms, including Ernst & Young, Accenture, and McKinsey. In these roles, he has been pivotal in transforming businesses by developing AI capabilities and leading complex, large-scale transformation programs across industries such as financial services, automotive, and high-performance manufacturing.

As a member of several advisory boards and a recognized thought leader, Huw frequently contributes to industry forums and publications, shaping the discourse on the future of AI. Beyond consulting, he is a driving force in AI innovation, working closely with academic institutions and startups to push the boundaries of what AI and ML can achieve in practical applications.