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Data Science:Data Mining & Natural Language Processing in R (Udemy.com)

Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples

4.6
( 196 Reviews )

Created by: Minerva Singh

Produced in 2021

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What you will learn

  • Perform the most important pre-processing tasks needed prior to machine learning in R
  • Carry out data visualization in R
  • Use machine learning for unsupervised classification in R
  • Carry out supervised learning by building classification and regression models in R
  • Evaluate the accuracy of supervised machine learning algorithms and compare their performance in R
  • Carry out sentiment analysis using text data in R

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Quality Score

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Overall Score : 92 / 100

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Course Description


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 techniques
  • Data 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..etc
  • Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE
  • Statistical analysis, statistical inference, and the relationships between variables.
  • Machine Learning, Supervised Learning, & Unsupervised Learning in R
  • Neural Networks for Classification and Regression
  • Web-Scraping using R
  • Extracting text data from Twitter and Facebook using APIs
  • Text mining
  • Common 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!

Who this course is for:
  • Students wishing to learn practical data science and machine learning in R
  • Students wishing to learn the underlying theory and application of data mining in R
  • Students interested in obtaining/mining data from sources such as Twiter
  • Students interested in pre-processing and visualizing real life data
  • Students wishing to analyze and derive insights from text data
  • Students interested in learning basic text mining and Natural Language Processing (NLP) in R
Read More product length iconTime: 13h

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Instructor Details

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Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler

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Reviews

4.6

196 total reviews

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By Takashi Suzuki on 11/7/2020

I think the course matches my expectations so far.

By Sujay Panda on 11/7/2020

Informative course

By Ramesh Jadhav on 11/7/2020

The course contents are very useful for practical application. The concept has been brilliantly explained by the instructor with practical examples.

By Manoj Kumar Sahu on 10/23/2020

Good teaching concepts

By Vishvajit Patil on 9/27/2020

GOOD

By Kunal Mahajan on 9/24/2020

This course was very much informative.

By Gabriel Sisto on 9/20/2020

Great course and instructor

By Anonymized User on 8/23/2020

I was looking for this kind of information for my research. the course perfectly matches with my requirement.

By Kaushal on 8/16/2020

Informative

By Elton Pillay on 8/8/2020

Course could be more direct. Some users might not understand the point or principles of variables. The introduction could be on the required programs and purpose of it. To some it up, keep it short and sweet.

By Greta Wong on 7/25/2020

A lot of data mining with R

By Rahul Tirupati on 6/27/2020

Predictive mean mapping method is just awesome

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Data Science:Data Mining & Natural Language Processing in R
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