|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/clustering-classification-with-machine-learning-in-r/
课程评论：没有评论
课程名称：使用R进行聚类与分类的机器学习 课程概述：本课程是学习监督学习和无监督学习的完整指南，使用R语言进行数据科学的实用操作，适合希望在大数据时代提升职业竞争力的学员。随着全球公司不断利用R语言分析海量信息，掌握R语言中的无监督与监督学习，能够为个人和公司带来竞争优势。 授课教师：Minerva Singh，牛津大学地理与环境专业硕士毕业，剑桥大学博士，拥有五年以上的数据分析实战经验，曾在国际同行评审期刊上发表论文。课程中，老师将深入讲解机器学习的各个方面，致力于克服当前R数据科学课程的不足。 课程内容：课程分为八个部分，覆盖R语言机器学习的所有重要方面，包括： - R框架与数据科学的全面介绍 - R中的数据结构及数据读取（CSV、Excel、HTML） - 数据预处理与清洗，包含去除空值及数据可视化 - 机器学习、监督学习与无监督学习 - 模型构建与选择等内容 课程不要求学员有R语言或统计机器学习的基础，讲解采用易于理解的实战方法，帮助学员处理真实数据而非虚构数据。完成课程后，学员能够熟练使用caret等数据科学包处理实际数据，掌握无监督学习、降维和监督学习等概念，并能充分利用课程中提供的代码与数据。 立即加入课程，开启R语言机器学习的探索之旅！
HERE IS WHY YOU SHOULD TAKE THIS COURSE:This course your complete guide to both supervised & unsupervised learning using R...That means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in R, you can give your company a competitive edge and boost your career to the next level.LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic...This course will give you a robust grounding in the main aspects of machine learning- clustering & classification. Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in Data Science! You will go all the way from carrying out data reading & cleaning to machine learning to finally implementing powerful machine learning algorithms and evaluating their performance using R.THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF R MACHINE LEARNING:• A full introduction to the R Framework for data science • Data Structures and Reading in R, including CSV, Excel and HTML data • How to Pre-Process and "Clean" data by removing NAs/No data,visualization • Machine Learning, Supervised Learning, Unsupervised Learning in R • Model building and selection...& MUCH MORE!By the end of the course, you'll have the keys to the entire R Machine Learning Kingdom!NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE REQUIRED:You'll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life.After taking this course, you'll easily use data science packages like caret to work with real data in R.You'll even understand concepts like unsupervised learning, dimension reduction and supervised learning. Again, we'll work with real data and you will have access to all the code and data used in the course. JOIN MY COURSE NOW!
