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PYTHON FOR AI AND DATA SCIENCE: A COMPREHENSIVE GUIDE
In this book, "Python for AI and Data A Comprehensive Guide", we aim to provide you with a comprehensive understanding of Python programming, specifically tailored for AI (Artificial Intelligence) and data science applications. Whether you are a novice programmer or an experienced developer, this book will equip you with the knowledge and skills needed to excel in the rapidly growing fields of AI and data science.
The book consists of sixteen carefully curated chapters, each focusing on different aspects of Python programming relevant to AI and data science. We begin with an introduction to Python, covering topics such as installation, environment setup, and the fundamentals of the language. From there, we delve into the basic elements of Python, including statements, objects, variables, and expressions, laying a strong foundation for your programming journey.
We then explore various data types in Python, such as numeric types, boolean type, strings, lists, tuples, dictionaries, sets, and frozensets. Understanding these compound data types is crucial for effective data manipulation and analysis in AI and data science projects.
Next, we guide you through control flow and iteration, helping you master the art of making decisions and repeating tasks. You will learn about conditional statements, loops, and the powerful features of Python's iteration structures.
In the chapters that follow, we delve into functions, file handling, modules, and packages, teaching you how to organize and structure your code effectively. We also introduce you to data manipulation and visualization techniques using popular third-party libraries such as NumPy, pandas, Matplotlib, Seaborn, and more. These tools will empower you to handle and analyze large datasets efficiently and create impactful visualizations to communicate your findings effectively.
As AI and data science rely heavily on advanced topics and libraries, we dedicate a chapter to cover web scraping, regular expressions, data manipulation with pandasql, image processing with OpenCV, reinforcement learning with OpenAI Gym, time series analysis with statsmodels, Conversational AI, and ChatGPT. These topics will expand your toolkit and enable you to tackle complex AI and data science projects with confidence. Finally, we address advanced topics such as packaging Python applications, creating executables, web application development, cloud deployment, performance optimization, and best practices. These topics will equip you with the necessary skills to take your AI and data science projects from development to production, ensuring they perform optimally in real-world scenarios.
To bridge the gap between theory and real-world applications, we present case studies that illustrate practical implementations of AI and data science techniques. These case studies cover a wide range of topics, including sentiment analysis of movie reviews, image classification with CNNs, predicting house prices with regression, customer segmentation with clustering, and building a chatbot. Through these case studies, you will gain valuable insights into solving real-world problems using Python.
To bridge the gap between theory and real-world applications, we present case studies that illustrate practical implementations of AI and data science techniques. These case studies cover a wide range of topics, including sentiment analysis of movie reviews, image classification with CNNs, predicting house prices with regression, customer segmentation with clustering, and building a chatbot. Through these case studies, you will gain valuable insights into solving real-world problems using Python.
We sincerely hope that "Python for AI and Data A Comprehensive Guide" serves as a valuable resource in your journey to becoming a proficient Python programmer.
The book consists of sixteen carefully curated chapters, each focusing on different aspects of Python programming relevant to AI and data science. We begin with an introduction to Python, covering topics such as installation, environment setup, and the fundamentals of the language. From there, we delve into the basic elements of Python, including statements, objects, variables, and expressions, laying a strong foundation for your programming journey.
We then explore various data types in Python, such as numeric types, boolean type, strings, lists, tuples, dictionaries, sets, and frozensets. Understanding these compound data types is crucial for effective data manipulation and analysis in AI and data science projects.
Next, we guide you through control flow and iteration, helping you master the art of making decisions and repeating tasks. You will learn about conditional statements, loops, and the powerful features of Python's iteration structures.
In the chapters that follow, we delve into functions, file handling, modules, and packages, teaching you how to organize and structure your code effectively. We also introduce you to data manipulation and visualization techniques using popular third-party libraries such as NumPy, pandas, Matplotlib, Seaborn, and more. These tools will empower you to handle and analyze large datasets efficiently and create impactful visualizations to communicate your findings effectively.
As AI and data science rely heavily on advanced topics and libraries, we dedicate a chapter to cover web scraping, regular expressions, data manipulation with pandasql, image processing with OpenCV, reinforcement learning with OpenAI Gym, time series analysis with statsmodels, Conversational AI, and ChatGPT. These topics will expand your toolkit and enable you to tackle complex AI and data science projects with confidence. Finally, we address advanced topics such as packaging Python applications, creating executables, web application development, cloud deployment, performance optimization, and best practices. These topics will equip you with the necessary skills to take your AI and data science projects from development to production, ensuring they perform optimally in real-world scenarios.
To bridge the gap between theory and real-world applications, we present case studies that illustrate practical implementations of AI and data science techniques. These case studies cover a wide range of topics, including sentiment analysis of movie reviews, image classification with CNNs, predicting house prices with regression, customer segmentation with clustering, and building a chatbot. Through these case studies, you will gain valuable insights into solving real-world problems using Python.
To bridge the gap between theory and real-world applications, we present case studies that illustrate practical implementations of AI and data science techniques. These case studies cover a wide range of topics, including sentiment analysis of movie reviews, image classification with CNNs, predicting house prices with regression, customer segmentation with clustering, and building a chatbot. Through these case studies, you will gain valuable insights into solving real-world problems using Python.
We sincerely hope that "Python for AI and Data A Comprehensive Guide" serves as a valuable resource in your journey to becoming a proficient Python programmer.
420 pages, Paperback
Published October 11, 2023
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