Artificial Intelligence, Machine Learning, and Deep Learning (eBook)

This book introduces AI, then explores machine learning, deep learning, natural language processing (NLP), and reinforcement learning. Readers learn about classifiers like logistic regression, k-NN, decision trees, random forests, and SVMs. It delves into deep learning architectures such as CNNs, RNNs, LSTMs, and autoencoders, with Keras-based code samples supplementing the theory.
Starting with a foundational AI overview, the course progresses into machine learning, explaining classifiers and their applications. It continues with deep learning, focusing on architectures like CNNs and RNNs. Advanced topics include LSTMs and autoencoders, essential for modern AI. The book also covers NLP and reinforcement learning, emphasizing their importance.
Understanding these concepts is vital for developing advanced AI systems. This book transitions you from beginner to proficient AI practitioner, combining theoretical knowledge and practical skills. Appendices on Keras, TensorFlow 2, and Pandas enrich the learning experience. By the end, readers will understand AI principles and be ready to apply them in real-world scenarios.

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Master AI, machine learning, and deep learning with practical insights into RNNs, LSTMs, NLP, and reinforcement learning.Key FeaturesIn-depth coverage of AI, machine learning, and deep learning conceptsPractical examples and hands-on tutorialsIntegration with Keras, TensorFlow, and Pandas for real-world applicationsBook DescriptionThis book introduces AI, then explores machine learning, deep learning, natural language processing (NLP), and reinforcement learning. Readers learn about classifiers like logistic regression, k-NN, decision trees, random forests, and SVMs. It delves into deep learning architectures such as CNNs, RNNs, LSTMs, and autoencoders, with Keras-based code samples supplementing the theory. Starting with a foundational AI overview, the course progresses into machine learning, explaining classifiers and their applications. It continues with deep learning, focusing on architectures like CNNs and RNNs. Advanced topics include LSTMs and autoencoders, essential for modern AI. The book also covers NLP and reinforcement learning, emphasizing their importance. Understanding these concepts is vital for developing advanced AI systems. This book transitions you from beginner to proficient AI practitioner, combining theoretical knowledge and practical skills. Appendices on Keras, TensorFlow 2, and Pandas enrich the learning experience. By the end, readers will understand AI principles and be ready to apply them in real-world scenarios.What you will learnUnderstand AI and its componentsApply machine learning algorithmsImplement deep learning modelsWork with RNNs and LSTMsExplore NLP techniquesUtilize Keras, TensorFlow, and Pandas effectivelyWho this book is forDevelopers, data scientists, and AI enthusiasts looking to deepen their knowledge of AI, machine learning, and deep learning. Basic understanding of programming and statistics is recommended. Familiarity with Python is beneficial.]]>