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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-datamining-natural-language-processing-in-r/
课程评论：没有评论
课程名称：数据科学：R语言中的数据挖掘与自然语言处理 课程概述： 本课程旨在帮助学员掌握数据科学、文本挖掘和自然语言处理的技能，重点使用R语言进行数据分析。您将学习如何进行预处理、可视化及机器学习任务，包括聚类、分类和回归，进而从文本数据和Twitter等社交媒体中提炼洞见，为您和您的公司提供竞争优势。 授课导师Minerva Singh，拥有牛津大学地理与环境硕士及剑桥大学热带生态与保护博士学位，拥有超过五年的数据科学实际经验。本课程与其他课程不同，除了机器学习，还涵盖数据挖掘、网络爬虫、文本挖掘和自然语言处理，确保学员获得全面的知识。 无论您是否有R或统计/机器学习的基础，课程都将从R数据科学的基础和技巧开始。在轻松易懂的实操方法指导下，您将应用来自不同来源的真实数据，而不仅仅是虚构的数据。通过此课程，您将熟练使用R语言中的各种数据处理包，如caret和dplyr，并学习使用常见的自然语言处理包提取文本数据中的洞见。 课程内容包括： - R语言及R Studio的基础知识 - 数据结构及读取CSV、Excel、JSON等数据 - 数据预处理与清洗 - 数据可视化技术，包括直方图、箱形图、散点图等 - 统计分析和变量关系 - 监督学习与非监督学习 - 神经网络分类与回归 - 使用R进行网络爬虫 - 从Twitter和Facebook提取文本数据 - 自然语言处理技术，如情感分析和主题建模 该课程大部分将集中在真实数据上的技术实现与结果解读，确保学员可以即时应用所学知识于实际项目中。课程提供的所有数据与代码均免费供学员使用，并将持续更新附加讲座。立即加入课程，提升您的数据科学技能！
MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R: Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. 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 (Tropical Ecology and Conservation).I have several 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 and use data science interchangeably with machine learning.This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS 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 packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you'll know it all: visualization, stats, machine learning, data mining, and neural networks! The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects. HERE IS WHAT YOU WILL GET: (a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools. (b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling. (c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques. (e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results. More Specifically, here's what's covered in the course: Getting started with R, R Studio and Rattle for implementing different data science techniquesData Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.How to Pre-Process and "Wrangle" your R data by removing NAs/No data, handling conditional data, grouping by attributes..etcCreating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MOREStatistical analysis, statistical inference, and the relationships between variables.Machine Learning, Supervised Learning, & Unsupervised Learning in R Neural Networks for Classification and RegressionWeb-Scraping using RExtracting text data from Twitter and Facebook using APIsText miningCommon Natural Language Processing techniques such as sentiment analysis and topic modelling We will spend some time dealing with some of the theoretical concepts related to data science. 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. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW!
