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OverviewMastering 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. Full Product DetailsAuthor: Jürgen Weichenberger , Huw KwonPublisher: BPB Publications Imprint: BPB Publications Dimensions: Width: 19.10cm , Height: 3.00cm , Length: 23.50cm Weight: 0.998kg ISBN: 9789355519832ISBN 10: 9355519834 Pages: 588 Publication Date: 16 October 2024 Audience: General/trade , General Format: Paperback Publisher's Status: Active Availability: In Print ![]() This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsReviewsAuthor InformationJürgen 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. Tab Content 6Author Website:Countries AvailableAll regions |