Machine Learning Artificial Intelligence by Reza Rawassizadeh (14 results)

paperback. Condition: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Condition: new. Paperback. Mastering AI, machine learning, and data science often means piecing together concepts scattered across countless resources, statistics, and visualizations to foundational models and large language models. This book, the result of eight years of effort, brings it all together in one accessible, engaging package. It clarifies artificial intelligence and data science, blending core mathematical principles with a clear, reader-friendly approach. Unlike traditional textbooks that lean heavily on equations and mathematical formalization, the author starts with minimal prerequisites, layering deeper math as the reader progresses. Each concept, algorithm, or model is unpacked through clear, hands-on examples that build the reader's skills step by step. It strikes a balance between theoretical foundations and practical application, serving as both an academic reference and a practical guide.Furthermore, the book uses humor, casual language, and comics to make the challenging concepts and topics relatable and fun. Any resemblance between the jokes and real life is pure coincidence, and no offense is intended.Table of ContentsPart I: Introduction & Preliminary RequirementsChapter 1: Basic ConceptsChapter 2: VisualizationChapter 3: Probability and StatisticsPart II: Unsupervised LearningChapter 4: ClusteringChapter 5: Frequent Itemset, Sequence Mining and Information RetrievalPart III: Data EngineeringChapter 6: Feature EngineeringChapter 7: Dimensionality Reduction and Data DecompositionPart IV: Supervised LearningChapter 8: Regression AnalysisChapter 9: ClassificationPart V: Neural NetworkChapter 10: Neural Networks and Deep LearningChapter 11: Self-Supervised Deep LearningChapter 12: Deep Learning Models and Applications (Text, Vision, and Audio)Part VI: Reinforcement LearningChapter 13: Reinforcement LearningPart VII: Other Algorithms and ConceptsChapter 14: Making Lighter Neural Network and Machine Learning ModelsChapter 15: Graph Mining AlgorithmsChapter 16: Concepts and Challenges of Working with Data This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Mastering AI, machine learning, and data science often means piecing together concepts scattered across countless resources, statistics, and visualizations to foundational models and large language models. This book, the result of eight years of effort, brings it all together in one accessible, engaging package. It clarifies artificial intelligence and data science, blending core mathematical principles with a clear, reader-friendly approach. Unlike traditional textbooks that lean heavily on equations and mathematical formalization, the author starts with minimal prerequisites, layering deeper math as the reader progresses. Each concept, algorithm, or model is unpacked through clear, hands-on examples that build the reader's skills step by step. It strikes a balance between theoretical foundations and practical application, serving as both an academic reference and a practical guide.Furthermore, the book uses humor, casual language, and comics to make the challenging concepts and topics relatable and fun. Any resemblance between the jokes and real life is pure coincidence, and no offense is intended.Table of ContentsPart I: Introduction & Preliminary RequirementsChapter 1: Basic ConceptsChapter 2: VisualizationChapter 3: Probability and StatisticsPart II: Unsupervised LearningChapter 4: ClusteringChapter 5: Frequent Itemset, Sequence Mining and Information RetrievalPart III: Data EngineeringChapter 6: Feature EngineeringChapter 7: Dimensionality Reduction and Data DecompositionPart IV: Supervised LearningChapter 8: Regression AnalysisChapter 9: ClassificationPart V: Neural NetworkChapter 10: Neural Networks and Deep LearningChapter 11: Self-Supervised Deep LearningChapter 12: Deep Learning Models and Applications (Text, Vision, and Audio)Part VI: Reinforcement LearningChapter 13: Reinforcement LearningPart VII: Other Algorithms and ConceptsChapter 14: Making Lighter Neural Network and Machine Learning ModelsChapter 15: Graph Mining AlgorithmsChapter 16: Concepts and Challenges of Working with Data This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condition: new. Paperback. Mastering AI, machine learning, and data science often means piecing together concepts scattered across countless resources, statistics, and visualizations to foundational models and large language models. This book, the result of eight years of effort, brings it all together in one accessible, engaging package. It clarifies artificial intelligence and data science, blending core mathematical principles with a clear, reader-friendly approach. Unlike traditional textbooks that lean heavily on equations and mathematical formalization, the author starts with minimal prerequisites, layering deeper math as the reader progresses. Each concept, algorithm, or model is unpacked through clear, hands-on examples that build the reader's skills step by step. It strikes a balance between theoretical foundations and practical application, serving as both an academic reference and a practical guide.Furthermore, the book uses humor, casual language, and comics to make the challenging concepts and topics relatable and fun. Any resemblance between the jokes and real life is pure coincidence, and no offense is intended.Table of ContentsPart I: Introduction & Preliminary RequirementsChapter 1: Basic ConceptsChapter 2: VisualizationChapter 3: Probability and StatisticsPart II: Unsupervised LearningChapter 4: ClusteringChapter 5: Frequent Itemset, Sequence Mining and Information RetrievalPart III: Data EngineeringChapter 6: Feature EngineeringChapter 7: Dimensionality Reduction and Data DecompositionPart IV: Supervised LearningChapter 8: Regression AnalysisChapter 9: ClassificationPart V: Neural NetworkChapter 10: Neural Networks and Deep LearningChapter 11: Self-Supervised Deep LearningChapter 12: Deep Learning Models and Applications (Text, Vision, and Audio)Part VI: Reinforcement LearningChapter 13: Reinforcement LearningPart VII: Other Algorithms and ConceptsChapter 14: Making Lighter Neural Network and Machine Learning ModelsChapter 15: Graph Mining AlgorithmsChapter 16: Concepts and Challenges of Working with Data This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. Machine Learning and Artificial Intelligence | Concepts, Algorithms and Models | Reza Rawassizadeh | Taschenbuch | Englisch | 2025 | Reza Rawassizadeh | EAN 9798992162110 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mastering AI, machine learning, and data science often means piecing together concepts scattered across countless resources, statistics, and visualizations to foundational models and large language models. This book, the result of eight years of effort, brings it all together in one accessible, engaging package. It clarifies artificial intelligence and data science, blending core mathematical principles with a clear, reader-friendly approach.Unlike traditional textbooks that lean heavily on equations and mathematical formalization, the author starts with minimal prerequisites, layering deeper math as the reader progresses. Each concept, algorithm, or model is unpacked through clear, hands-on examples that build the reader's skills step by step. It strikes a balance between theoretical foundations and practical application, serving as both an academic reference and a practical guide.Furthermore, the book uses humor, casual language, and comics to make the challenging concepts and topics relatable and fun. Any resemblance between the jokes and real life is pure coincidence, and no offense is intended.Table of ContentsPart I: Introduction & Preliminary RequirementsChapter 1: Basic ConceptsChapter 2: VisualizationChapter 3: Probability and StatisticsPart II: Unsupervised LearningChapter 4: ClusteringChapter 5: Frequent Itemset, Sequence Mining and Information RetrievalPart III: Data EngineeringChapter 6: Feature EngineeringChapter 7: Dimensionality Reduction and Data DecompositionPart IV: Supervised LearningChapter 8: Regression AnalysisChapter 9: ClassificationPart V: Neural NetworkChapter 10: Neural Networks and Deep LearningChapter 11: Self-Supervised Deep LearningChapter 12: Deep Learning Models and Applications (Text, Vision, and Audio)Part VI: Reinforcement LearningChapter 13: Reinforcement LearningPart VII: Other Algorithms and ConceptsChapter 14: Making Lighter Neural Network and Machine Learning ModelsChapter 15: Graph Mining AlgorithmsChapter 16: Concepts and Challenges of Working with Data. …