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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-for-social-scientists/
课程评论：没有评论
**课程名称：** 使用R语言进行机器学习：学习数据科学 **课程概述：** 本课程旨在打破机器学习与编程技术领域的壁垒，让来自哲学、艺术、社会科学等非技术背景的学习者也能轻松掌握这门强大的学科。机器学习不仅是技术专家的专属，更是提升研究和决策能力的关键工具。它帮助我们理解人类和机器的决策过程，构建数学模型，通过数据优化决策，并进行数据驱动的科学研究。 机器学习是人工智能的敲门砖，也是一门融合了计算机科学、数学、统计学、心理学和管理学的跨学科领域。然而，掌握这些领域的知识对许多人来说是一项挑战。本课程特别为社会科学背景的学习者设计，以“从零到高级”的方式，深入浅出地讲解机器学习。课程内容从基础概念出发，通过日常实例和基础工具（如Excel计算演示）与R语言相结合，帮助学习者建立起知识的联系，消除对编程的恐惧。 **课程主要内容：** * 机器学习基础概念 * 机器学习的应用领域 * 机器学习背后的统计学原理 * 监督学习算法 * 无监督学习算法 * 利用R语言实现机器学习算法 * 创建训练集和测试集，以及训练机器学习模型 * 提高机器学习模型的准确性 * 线性回归算法，包括参数的手动、Excel及R语言计算 * K近邻（KNN）分析 * K近邻算法的数学原理 * 模型敏感性和特异性的估算 * 在R语言中实现KNN算法 * 以及更多相关内容 **课程价值：** 随着当今行业对机器学习人才需求的不断增长，机器学习领域提供了丰厚的回报，专业人士的年薪往往超过10万美元。本课程将为您提供一个平缓的入门途径，帮助您开启机器学习的学习之旅。立即注册，开启您在机器学习领域的探索。
"We are bringing technology to philosophers and poets."Machine Learning is usually considered to be the forte of professionals belonging to the programming and technology domain. People from arts and social science with no background in programming/technology often find it challenging to learn Machine Learning. However, Machine learning is not for technologists and programmers only. It is for everyone who wants to be a better researcher and decision-maker.Machine Learning is for anyone looking to model how humans and machines make decisions, develop mathematical models of decisions, improve decision-making accuracy based on data, and do science with data.Machine Learning brings you closer to the fascinating world of artificial intelligence. Machine Learning is a cross-disciplinary field encompassing computer science, mathematics, statistics, psychology, and management. It's currently tough for normal learners to understand so many subjects, making Machine Learning inaccessible to many, especially those from social science backgrounds.We built this course, "Machine Learning for Social Scientists," to help learners master this topic without getting stuck in its technicalities or fear of coding. This course is built as a scratch to the advanced level course for Machine Learning. All the topics are explained with the basics. The instructor creates a connection with everyday instances and fundamental tools so that learners feel connected to their previous learning. For example, we demo some Excel calculations to ensure learners can see the connection between Excel spreadsheet analysis and Machine Learning using R language.The course covers the following topics:· Fundamentals of Machine Learning· Applications of Machine Learning· Statistical concepts underlying Machine Learning· Supervised Machine Learning Algorithms· Unsupervised Machine Learning Algorithms· How to Use R to Implement Machine Learning Algorithms· How to create Training and Testing datasets and train Machine Learning Models· How to improve the accuracy of Machine Learning Models· Linear Regression Algorithm· Calculation of Parameters of Linear Regression Model manually, using Excel and R· K Nearest Neighbor (KNN) Analysis· Understanding Mathematics behind K Nearest Neighbor Analysis· Estimating sensitivity and specificity of the model· Implementing KNN Algorithm in R· Many moreAccording to various estimates, Machine Learning is among the highest-paid job in the industry, and salaries of Machine Learning professionals could usually be above US$1,00,000 per annum. If you are looking forward to a course that can get you gently started with Machine Learning, this course is for you. To join the course, click on the Sign Up button and start your journey in Machine Learning from today.
