Data Science: Data Mining & Natural Language Processing in R

BY
Udemy

Develop your knowledge on the machine learning techniques in R for data and text mining with the natural language processing concepts in this course.

Mode

Online

Fees

₹ 3699

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

The Data Science: Data Mining & Natural Language Processing in R online course is a study about the clustering, classification, and regression of data in R with the help of data preprocessing, data visualization, and machine learning. The course is provided by the online education platform Udemy for the students who are interested in learning data science with R fundamentals and strategies.

The course curriculum is developed with thirteen hours of video content, four articles, and ten downloadable resources that can be learned by the students at their speed and convenience. The educator for this online program is Minerva Singh who will guide the students with discussions on data mining, web-scraping, text mining, and natural language processing. 

The Data Science: Data Mining & Natural Language Processing in R training program enables the students to gain knowledge through experimental learning strategies and applications. The candidates are given the course certificate after completing the online training.

The highlights

  • Online course
  • Independent learning
  • Professional guidance
  • Downloadable resources
  • Full-time course access
  • Course certificate

Program offerings

  • Videos
  • Lectures
  • Full-time course access
  • Articles
  • Quizzes
  • Mobile access
  • Tv access
  • Certificate of completion

Course and certificate fees

Fees information
₹ 3,699

The Data Science: Data Mining & Natural Language Processing in R course curriculum is available to the students after the payment of the course fee.

Data Science: Data Mining & Natural Language Processing in R fee structure

Course fee

Rs 3699

certificate availability

Yes

certificate providing authority

Udemy

Who it is for

The Data Science: Data Mining & Natural Language Processing in R online certification program is developed for students who wish to learn about data science and machine learning in R, and implementation of data mining in R. The course is for the students who would like to learn about the techniques of getting data from Twitter, and pre-processing and visualizing real-life data. The course benefits data scientists to improve their industry knowledge and skills.

Eligibility criteria

The Data Science: Data Mining & Natural Language Processing in R certification program requires the students to have experience with handling R and RStudio, they should have the skills to install and read in packages in R. The candidates applying should have the knowledge of statistical data analysis, data visualization and summarizing in R.

Certificate qualifying details

The students of the ‘Data Science: Data Mining & Natural Language Processing in R’ online training program are provided with the course completion certificate after finishing the course.

What you will learn

Data science knowledge Knowledge of data mining Knowledge of data visualization Knowledge of nlp modelling Machine learning Statistical skills

The Data Science: Data Mining & Natural Language Processing in R curriculum is developed for the candidates of the program to engage themselves with the data science concepts of data mining and natural language processing in R. The students will gain an understanding of the following aspects,

  • Carrying out primary preprocessing tasks required before the machine learning in R.
  • Unsupervised classification in R is done with the help of machine learning techniques.
  • Analyze the precision of supervised machine learning algorithms and evaluate their performance in R.
  • Perform data visualization in R and supervised learning by creating classification and regression models in R.
  • Execute sentiment analysis with the text data in R.

The syllabus

Introduction to the course: The key concepts and software tools

  • Introduction
  • Data and Scripts For the Course
  • Introduction to R and RStudio
  • Start with Rattle
  • Troubleshooting For Rattle
  • Conclusion to Section 1

Reading in data from different sources in R

  • Read in data from CSV and Excel Files
  • Read Data from a Database
  • Read Data from JSON
  • Read in Data from Online CSVs
  • Read in Data from Online HTML Tables-Part 1
  • Read in Data from Online HTML Tables-Part 2
  • Read Data from Other Sources
  • Conclusions to Section 2

Exploratory data analysis and data visualization in R

  • Remove NAs
  • More Data Cleaning
  • Exploratory Data Analysis(EDA): Basic Visualizations with R
  • More Exploratory Data Analysis with xda
  • Introduction to dplyr for Data Summarizing-Part 1
  • Introduction to dplyr for Data Summarizing-Part 2
  • Data Exploration & Visualization With dplyr & ggplot2
  • Pre-Processing Dates-Part 1
  • Pre-Processing Dates-Part 2
  • Plotting Temporal Data in R
  • Twist in the (Temporal) Data
  • Associations Between Quantitative Variables- Theory
  • Testing for Correlation
  • Evaluate the Relation Between Nominal Variables
  • Cramer's V for Examining the Strength of Association Between Nominal Variable
  • Section 3 Quiz

Data mining for patterns and relationships

  • Association Mining with Apriori
  • Apriori with Real Data
  • Visualize the Rules
  • Association Mining with Eclat
  • Eclat with Real Data

Machine learning for Data Science

  • How is machine learning different from statistical data analysis
  • What is Machine Learning(ML) about? some theoretical pointers

Unsupervised classification - R

  • K-means Clustering
  • Fuzzy K-Means Clustering
  • Weighted K-Means Clustering
  • Hierarchical Clustering in R
  • Expectation-Maximization (EM) in R
  • Use Rattle for Unsupervised Clustering
  • Conclusions to Section 6
  • Section 6 Quiz

Dimension reduction

  • PCA
  • Removing Highly Correlated Predictor Variables
  • Variable Selection Using LASSO Regression
  • Variable Selection With FSelector
  • Boruta Analysis for Feature Selection
  • Conclusions to Section 7
  • Section 7 Quiz

Supervised learning theory

  • Some basic supervised learning concepts
  • Pre-processing for supervised learning

Supervised learning: Classification

  • What are GLMs?
  • Logistic Regression Models as Binary Classifiers
  • Linear Discriminant Analysis (LDA)
  • Binary Classifier with PCA
  • Obtain Binary Classification Accuracy Metrics
  • Our Multi-class Classification Problem
  • Classification Trees
  • More on Classification Tree Visualization
  • Decision Trees
  • Random Forest (RF) classification
  • Examine Individual Variable Importance for Random Forests
  • GBM Classification
  • Support Vector Machines (SVM) for Classification
  • More SVM for Classification
  • Section 9 Quiz

Supervised learning: Regression

  • Ridge Regression in R
  • LASSO Regression in R
  • Generalized Additive Models (GAMs) in R
  • Boosted GAMs
  • MARS Regression
  • CART-Regression Trees in R
  • Random Forest (RF) Regression
  • GBM Regression
  • Compare Models
  • Conclusions to Section 10

Introduction to Artificial Neural Networks (Ann)

  • What are Artificial Neural Networks?
  • Neural Network for Binary Classifications
  • Neural Network with PCA for Binary Classifications
  • Neural Network for Regression
  • More on Neural Networks- with neuralnet
  • Identify Variable Importance in Neural Networks

More Web-scraping and text Data Mining

  • Read in Text Data from an HTML Page
  • Explore Amazon with R
  • More Webscraping With rvest-IMDB Webpage
  • Prior to Mining Data from Twitter
  • Extract Tweets Using R
  • More Twitter Data Extraction Using R
  • Get Data from Facebook Using R
  • Conclusions to Section 12

Gaining Insights from Text Data- Text Mining and Natural Language Processing (NL

  • Explore Tweet Data
  • Visualize Tweet Sentiment Wordcloud- India's Demonetization Policy
  • More Wordclouds: Amazon Review Data
  • Word Frequency in Text Data
  • Tweet Sentiments- India's Demonetization Policy
  • Sentiment Analysis of Mugabe Tweets
  • Tweet Sentiments- Mugabe's Ouster
  • Examine the Polarity of Text
  • Polarity of Individual Tweets
  • Topic Modelling a Document
  • Topic Modelling Multiple Documents
  • Conclusion to Section 13

Text Data and Machine Learning

  • EDA With Text Data
  • Identify Deceptive Reviews With Supervised Classification
  • Identify Spam Emails with Supervised Classification

MisceLLaneous Lectures

  • 3D Scatterplots
  • Getting Acquainted with Github Desktop
  • Data Editing Within R
  • Group By Time

Admission details

The students who are interested in joining the ‘Data Science: Data Mining & Natural Language Processing in R’ online program have to register online on the Udemy website,

Step 1: Find the course page on the official website of Udemy using the link,

https://www.udemy.com/course/data-science-datamining-natural-language-processing-in-r/

Step 2: Click on the ‘Buy Now’ option

Step 3: Fill in the relevant information and complete the registration.


Filling the form

The students applying for the ‘Data Science: Data Mining & Natural Language Processing in R’ training will need to enter their name, email address, and the password of the Udemy course account to join the course.

How it helps

The Data Science: Data Mining & Natural Language Processing in R certification helps the students acquire advanced knowledge of data science and R-based tools and the skills to carry out visualization of tasks for data modeling. The students are familiarized with the concepts of natural language processing, data analysis and interpretation, data mining, and text mining.

Instructors

Ms Minerva Singh

Ms Minerva Singh
Data Scientist
Udemy

Other Masters, Ph.D, M.Phil.

FAQs

Which online education platform offers the course on ‘Data Science:Data Mining & Natural Language Processing in R’?

The course is provided by Udemy.

What is the duration of the ‘Data Science:Data Mining & Natural Language Processing in R’ online course?

The course curriculum consists of video lectures, articles, and other resources that can be studied at your speed.

What is the eligibility to join the ‘Data Science:Data Mining & Natural Language Processing in R’ certification program?

The applicants are required to have background knowledge of R, RStudio, install and read packages in R, statistical data analysis, and data visualization.

Will I get a course certificate for the ‘Data Science:Data Mining & Natural Language Processing in R’ training?

Yes, you will receive the certificate of completion after the training.

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