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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
This book offers a detailed exploration of Artificial Intelligence (AI) and Machine Learning (ML), presenting the foundational principles that define these rapidly evolving fields. It covers essential AI concepts such as neural networks, natural language processing, robotics, and expert systems, while providing a deep dive into various machine learning techniques, including supervised, unsupervised, and reinforcement learning.Beginning with the history and development of AI, the book explains core techniques such as search algorithms, knowledge representation, and decision-making processes. Moving to ML, it introduces modern learning algorithms, classification techniques, and models like decision trees, support vector machines, and neural networks. Advanced topics such as deep learning, convolutional networks, and recurrent neural networks are also covered.Additionally, the book discusses the ethical implications of AI and ML, challenges in development, and their transformative impact across different sectors, from healthcare to finance.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
This book offers a detailed exploration of Artificial Intelligence (AI) and Machine Learning (ML), presenting the foundational principles that define these rapidly evolving fields. It covers essential AI concepts such as neural networks, natural language processing, robotics, and expert systems, while providing a deep dive into various machine learning techniques, including supervised, unsupervised, and reinforcement learning.Beginning with the history and development of AI, the book explains core techniques such as search algorithms, knowledge representation, and decision-making processes. Moving to ML, it introduces modern learning algorithms, classification techniques, and models like decision trees, support vector machines, and neural networks. Advanced topics such as deep learning, convolutional networks, and recurrent neural networks are also covered.Additionally, the book discusses the ethical implications of AI and ML, challenges in development, and their transformative impact across different sectors, from healthcare to finance.