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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-data-science-and-machine-learning-b/
课程评论：没有评论
课程名称：数据科学与机器学习导论 课程概述：本课程全面介绍了数据科学与机器学习的交集，理论、数值方法（编码）和实际应用相结合，旨在为希望建立现代数据科学和机器学习算法背后的概念、统计学和数学知识的学生和初学者打下坚实基础。课程无需先前经验，适合希望迈出学习旅程的初学者。 课程内容包括： - 数据科学基础 - 数据可视化与叙事 - 线性与非线性回归方法 - 分类技术的探索，包括决策树、随机森林和神经网络等强大工具，以获取数据洞察。 - 深入无监督学习，利用谱聚类等创新聚类方法发现数据中的隐藏模式和分组。 课程结束时，学生将能够： - 应用定量建模和数据分析技术解决现实问题。 - 通过数据可视化有效沟通发现结果。 - 展示在应用工程中使用的统计数据分析技术的熟练程度。 - 利用数据科学原则应对工程挑战。 - 使用现代编程语言和计算工具分析大数据。 - 理解关键概念，并深入学习经典机器学习算法。 - 实施经典机器学习算法以创造智能系统。
This course provides a thorough introduction to the intersection of data science and machine learning, balancing theory, numerical methods (coding), and real-world applications. It is designed for students and beginners who want to build a strong foundation in the concepts, statistics, and mathematics that support modern data science and machine learning algorithms.No prior experience is required; this course starts with the fundamentals, making it an excellent choice for beginners ready to embark on their learning journey.The course covers essential topics, including:- The basics of data science- Data visualisation and storytelling- Linear and non-linear regression methods- Explore the world of classification techniques with powerful tools like decision trees, random forests, and neural networks to unlock insights from your data. - Dive into unsupervised learning, where you can discover hidden patterns and groupings in your data using innovative clustering methods like spectral clustering. By the end of this course, students will be able to:- Apply quantitative modelling and data analysis techniques to solve real-world problems.- Effectively communicate findings through data visualisation.- Demonstrate proficiency in statistical data analysis techniques used in applied engineering.- Utilise data science principles to tackle engineering challenges.- Employ modern programming languages and computational tools to analyse big data.- Understand key concepts and gain in-depth knowledge of classical machine learning algorithms.- Implement classic machine learning algorithms to create intelligent systems.
