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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-and-machine-leaning-principles-for-science/
课程评论：没有评论
课程名称：自然科学的数据科学与机器学习原理 课程概述： 《自然科学的数据科学与机器学习原理》课程旨在将传统科学学科与快速发展的数据科学（DS）和机器学习（ML）领域结合起来。由于研究越来越依赖于大型数据集和先进的计算方法，科学家们必须学习如何利用数据科学和机器学习技术来提高自己的工作效果。本课程为生物、化学、物理和环境科学等领域的科学家和研究人员提供了数据科学和机器学习的关键概念的扎实入门。 课程内容： 参与者将学习数据分析的基础知识，包括数据收集、清理和可视化，随后将进入能够帮助识别数据模式和进行预测的机器学习算法。课程不要求具备编程技能，重点侧重于基本理论概念。课程结构分为六个主要部分： 1. **导言**：介绍课程的主要特征、内容和学习方式。 2. **核心DS/ML概念**：讲解变量、数据缩放、训练、数据集和数据可视化等基本概念。 3. **分类**：讨论决策树、随机森林、朴素贝叶斯和KNN等关键分类算法，提供科学研究中的应用实例。 4. **回归**：简要介绍线性回归和多元线性回归，讨论主要概念及其在科学中的相关示例。 5. **聚类**：重点介绍标准聚类和层次聚类方法，并提供科学应用的实践实例。 6. **神经网络**：介绍神经网络的生物启发及常见架构，如前馈神经网络（FNN）、卷积神经网络（CNN）、递归神经网络（RNN）和霍普菲尔德网络。 本课程帮助科学家们掌握数据科学和机器学习的基础，为他们在各自领域的研究打下坚实基础。
The course "Principles of Data Science and Machine Learning for Natural Sciences" is designed to connect traditional scientific disciplines with the rapidly growing fields of Data Science (DS) and Machine Learning (ML). As research increasingly depends on large datasets and advanced computational methods, it's becoming essential for scientists to know how to leverage DS and ML techniques to improve their work.This course offers a solid introduction to the key concepts of Data Science and Machine Learning, specifically aimed at scientists and researchers in areas like biology, chemistry, physics, and environmental science. Participants will learn the basics of data analysis, including data collection, cleaning, and visualization, before moving on to machine learning algorithms that can help identify patterns and make predictions from data.The course doesn't require any programming skills and focuses on fundamental theoretical concepts. It's structured into six main sections:1. Introduction We'll start by introducing the course, covering its main features, content, and how to follow along.2. Core DS/ML Concepts We'll go over basic concepts like variables, data scaling, training, datasets, and data visualization.3. Classification In this section, we'll discuss key classification algorithms such as decision trees, random forests, Naive Bayes, and KNN, with examples of how they can be applied in scientific research.4. Regression We'll briefly cover linear and multiple linear regression, discussing the main ideas and providing examples relevant to science.5. Clustering This section will focus on standard and hierarchical clustering methods, along with practical examples for scientific applications.6. Neural Networks Finally, we'll introduce neural networks, discussing their biological inspiration and common architectures like Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Hopfield Networks.
