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所在平台: Udemy |
课程主页: https://www.udemy.com/course/species-distribution-models-with-gis-machine-learning-in-r/
课程评论：没有评论
课程名称：使用R中的GIS和机器学习进行物种分布模型 课程概览：您是一名生态学家或保护主义者，想学习使用R进行GIS和机器学习吗？您希望进行栖息地适宜性制图吗？该课程旨在帮助您掌握使用R进行生态数据访问和GIS分析的技能，并实现实践中的机器学习模型。授课教师MINERVA SINGH，牛津大学地理与环境专业硕士，剑桥大学热带生态与保护博士，拥有多年真实空间数据分析经验。 课程内容将使用来自马来西亚半岛的实际空间数据，为您提供真实数据下的栖息地适宜性制图实践，结合经典SDM模型MaxEnt和机器学习算法如随机森林。无论您的技能水平如何，您都将能够在自己的项目中应用空间数据和机器学习分析，从而提升您在GIS和机器学习方面的专业能力并吸引潜在雇主。 本课程的特色在于，这是唯一一个能够让您在R中实施常见机器学习算法的课程，同时您将接触物种分布模型（SDM）及空间数据分析技术。通过该课程，您将掌握GIS和机器学习的最新技术，以简单有趣的方式学习，甚至非生态学家也可以进行实际的机器学习技术操作。 课程结构包括： 1. 导言：介绍SDM和栖息地适宜性制图 2. SDM的GIS基础：包括如何通过R访问物种存在数据 3. SDM的栅格和空间数据预处理 4. 经典SDM技术：包括MaxENT和Bioclim的实现 5. 栖息地适宜性机器学习模型：实施和解释常见机器学习技术，以构建马来西亚半岛鸟类的栖息地适宜性图 本课程注重实践，尽量减少理论部分，使您能在真实数据上实施技术并解读结果。每个视频后，您将学习到新概念或技术，以便应用于自己的项目。欲了解更多，快来加入我们！如有任何不满意，Udemy提供30天无条件退款保证，您没有任何风险。点击注册按钮，让我们在课程中见！
Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R? Are you an ecologist/conservationist looking to carry out habitat suitability mapping?Are you an ecologist/conservationist looking to get started with R for accessing ecological data and GIS analysis?Do you want to implement practical machine learning models in R? Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R. My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals. In this course, actual spatial data from Peninsular Malaysia will be used to give a practical hands-on experience of working with real life spatial data for mapping habitat suitability in conjunction with classical SDM models like MaxEnt and machine learning alternatives such as Random Forests. The underlying motivation for the course is to ensure you can put spatial data and machine learning analysis into practice today. Start ecological data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your GIS and Machine Learning skills in R. So Many R based Machine Learning and GIS Courses Out There, Why This One? This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real ecological data in R. Plus, you will gain exposure to working your way through a common ecological modelling technique- species distribution modelling (SDM) using real life data. Students will also gain exposure to implementing some of the most common Geographic Information Systems (GIS) and spatial data analysis techniques in R. Additionally, students will learn how to access ecological data via R. You will learn to harness the power of both GIS and Machine Learning in R for ecological modelling. I have designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Yes, even non-ecologists can get started with practical machine learning techniques in R while working their way through real data. What you will Learn in this Course This is how the course is structured: Introduction - Introduction to SDMs and mapping habitat suitabilityThe Basics of GIS for Species Distribution Models (SDMs) - You will learn some of the most common GIS and data analysis tasks related to SDMs including accessing species presence data via RPre-Processing Raster and Spatial Data for SDMs - Your R based GIS training and will continue and you will earn to perform some of the most common GIS techniques on raster and other spatial dataClassical SDM Techniques - Introduction to the classical models and their implementation in R (MaxENT and Bioclim)Machine Learning Models for Habitat Suitability - Implement and interpret common ML techniques to build habitat suitability maps for the birds of Peninsular Malaysia. It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success. And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we'll see you in side the course.
