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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-powerhouse-gen-ai-from-basics-to-advanced-programming/
课程评论：没有评论
课程名称：Python Powerhouse Gen AI 从基础到高级编程 课程概述：欢迎参加《掌控机器灵感：用于生成艺术与设计的Python编程》课程，这是一门全面的课程，旨在通过 Python 编程的力量结合艺术与科技。该课程专为希望利用 Python 和生成 AI 创建惊人视觉艺术和创新设计解决方案的学员精心设计。您将从 Python 的基础知识开始，逐步深入到生成艺术和设计的高级技术。无论您是初学者还是有经验的程序员，本课程都提供了一条结构化的路径，以掌握 Python 的基本特性并将其应用于生成艺术的创意领域。 课程分为多个部分： 第一部分：Python中的列表和元组 了解 Python 中的基础数据结构，即列表和元组，学习如何创建、操作和有效利用这些结构。通过实践练习和案例研究，将学习列表的排序、索引和切片操作，以及了解元组的不可变性。 第二部分：Python中的集合和字典 深入了解更高级的数据结构：集合和字典。学习如何定义和操作集合，处理并集、交集和差集等各种操作，以及管理具有键值对的字典，从而优化数据管理策略。 第三部分：日期和时间操作 掌握使用 Python 处理日期和时间数据的技巧，学习如何格式化和操作日期时间对象，并在真实数据场景中应用这些技能，包括处理客户流失预测的案例研究。 第四部分：函数式编程 探索 Python 的函数式编程范式，关注 lambda 函数、map、reduce 和 filter 操作，了解如何简化代码并高效地进行数据转换。 第五部分：高级 Python 编程 扩展您的 Python 专业知识，学习递归、特征工程和数据分析等高级编程概念，解决使用迭代和递归方法的握手计算问题，了解机器学习模型的日期时间特征工程。 第六部分：Python 库与数据分析 熟练使用 Python 的标准库进行数学运算、随机数生成和文件处理。通过实际示例探索探索性数据分析技术，并了解如何有效管理和清理数据。 第七部分：高级数据可视化技术 学习高级可视化技术以解释和呈现数据洞察力，涵盖分布图、KDE图、联合图以及异常值识别，掌握创建引人注目的数据视觉呈现的技能。 第八部分：模型构建与评估 最终应用您的技能进行机器学习模型的构建与评估，学习数据预处理、模型训练与评估过程，专注于逻辑回归及其评估指标。 课程结束后，您将对 Python 编程及其在生成艺术与设计中的应用有扎实的理解，具备创造创新艺术、处理复杂数据以及将高级技术应用于现实问题的能力。无论您是希望在数据科学、艺术还是设计领域发展，这门课程都将为您提供所需的基础和高级技能。
Python Powerhouse Gen AI From Basics to Advanced ProgrammingWelcome to Master the Machine Muse: Python Programming for Generative Art & Design, a comprehensive course designed to blend the realms of art and technology through the power of Python programming. This course is meticulously crafted for those who wish to harness the capabilities of Python and generative AI to create stunning visual art and innovative design solutions.In this course, you'll embark on a journey that starts with the fundamentals of Python and progresses through advanced techniques in generative art and design. Whether you're a beginner or an experienced programmer, this course provides a structured pathway to mastering Python's essential features and applying them to the creative field of generative art.Section 1: Lists and Tuples in PythonBegin your journey by understanding the foundational data structures in Python-lists and tuples. You'll explore how to create, manipulate, and utilize these structures effectively. Through practical exercises and case studies, you'll learn to perform operations such as sorting, indexing, and slicing lists, as well as understand the immutable nature of tuples. This section provides a solid grounding in Python's core data types, essential for any advanced programming tasks.Section 2: Sets and Dictionaries in PythonDelve into more advanced data structures: sets and dictionaries. Learn to define and manipulate sets, handle various operations like union, intersection, and difference, and manage dictionaries with key-value pairs. This section equips you with the skills to handle more complex data scenarios and optimize your data management strategies.Section 3: Date and Time ManipulationsMaster the art of handling date and time data with Python. You'll learn how to format and manipulate date-time objects, extract meaningful components, and apply these skills in real-world data scenarios. This section includes a case study on customer churn prediction, where you'll practice data preprocessing and feature engineering techniques crucial for accurate data analysis.Section 4: Functional ProgrammingExplore functional programming paradigms with Python, focusing on lambda functions, map, reduce, and filter operations. Understand how to apply these concepts to simplify code and perform efficient data transformations. This section provides a deep dive into functional programming techniques, offering practical examples and applications.Section 5: Advanced Python ProgrammingExpand your Python expertise with advanced programming concepts such as recursion, feature engineering, and data analysis. You'll tackle problems like handshake calculations using iterative and recursive methods, and learn to engineer date-time features for machine learning models. This section also includes an in-depth look at the IRIS dataset, enhancing your understanding of evaluation metrics like precision, recall, and AUC ROC.Section 6: Python Libraries and Data AnalysisGain proficiency in using Python's standard libraries for mathematical operations, random number generation, and file handling. Explore exploratory data analysis techniques through practical examples, such as football play analysis, and understand how to manage and clean data effectively.Section 7: Advanced Data Visualization TechniquesLearn advanced visualization techniques to interpret and present data insights. This section covers distribution plots, KDE plots, joint plots, and the identification of outliers. You'll develop skills to create compelling visual representations of data, aiding in both analysis and storytelling.Section 8: Model Building and EvaluationFinally, apply your skills to build and evaluate machine learning models. Learn the process of data preprocessing, model training, and evaluation with a focus on logistic regression. Understand key evaluation metrics and how to interpret them to improve your model's performance.By the end of this course, you will have a robust understanding of Python programming and its application in generative art and design. You will be equipped with the skills to create innovative art, handle complex data, and apply advanced techniques to real-world problems. Whether you aim to pursue a career in data science, art, or design, this course will provide the foundational and advanced skills needed to excel in these fields.
