40 Algorithms Every Data, Scientist Should KuowNavigatiiig through essential AI and ML algorithms, Weichenberger J., Kwon H., 2025

40 Algorithms Every Data, Scientist Should KuowNavigatiiig through essential AI and ML algorithms, Weichenberger J., Kwon H., 2025.
     
    Building Artificial Intelligence and Machine Learning Solutions is a complex task that requires a comprehensive understanding of the latest technologies and alogorithms available to us. Artificial Intelligence has become an increasingly powerful tool over the last couple of years and as such the amount of algorithsm available to us have explode.
This book is designed to provide a comprehensive guide through the world of Artificial Intelligence Algorithms and be a practical and hands-on support to every new data scientist as well as experienced data scientists. It covers a wide range of topics, including the basic definition of Artificial Intelligence and Machine Learning, basic data concepts, and basic and advanced algorithms for supervised, unsupervised, semi-supervised, and reinforcement learning algorithms.




Supervised learning algorithms.
Supervised learning algorithms are a class of AI and ML algorithms that learn from labeled training data to make predictions or classify new, unseen data. In supervised learning, the dataset used for training consists of input data (features) along with their corresponding output labels or target values. The goal is to learn a mapping between the input data and the desired output based on the provided examples.

Supervised learning algorithms aim to generalize from the labeled training data and make accurate predictions or classifications on unseen data by capturing the patterns, relationships, and dependencies present in the training set. These algorithms can be used for tasks such as regression (predicting continuous values) or classification (predicting discrete class labels).

ОГЛАВЛЕНИЕ.
Cover.
Title Page.
Copyright Page.
Dedication Page.
About the Authors.
About the Reviewer.
Acknowledgements.
Preface.
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.  0utlook into the Future: Quantum Machine Learning.
Index.



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