Data Science:Data Mining & Natural Language Processing in R
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Data Science:Data Mining & Natural Language Processing in R at Eduonix Overview
Duration | 13 hours |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Beginner |
Credential | Certificate |
Data Science:Data Mining & Natural Language Processing in R at Eduonix Highlights
- Start instantly and learn at your own schedule.
- Lifetime Access. No Limits!
- Learn online from any location with practical examples
- Learn visualization, stats, machine learning, data mining, and neural networks the easiest way
Data Science:Data Mining & Natural Language Processing in R at Eduonix Course details
- Use machine learning in R for data/text mining and natural language processing
- Learn clustering, classification and regression in R Pull out insights from text data and Twitter Conveniently use packages like caret, dplyr to work with real data in R
- Learn to use common NLP packages for extracting insights from text data
- This course will help you implement the methods using real data obtained from different sources.After taking this course, you will 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.
Data Science:Data Mining & Natural Language Processing in R at Eduonix Curriculum
Section 1 : 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
Conclusion to Section 1
Section 2 : 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
Section 3 : 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 4 : Data Mining for Patterns and Relationships
What is Data Mining?
Association Mining with Apriori
Apriori with Real Data
Visualize the Rules
Association Mining with Eclat
Eclat with Real Data
Section 5 : Machine Learning for Data Science
How is Machine Learning Different from Statistical Data Analysis?
What is Machine Learning (ML) About? Some Theoretical Pointers
Section 6 : 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 7 : Dimension Reduction
Dimensionality Reduction-theory
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 8 : Supervised Learning Theory
Some Basic Supervised Learning Concepts
Pre-processing for Supervised Learning
Section 9 : Supervised Learning: Classification
What are GLMs?
Logistic Regression Models as Binary Classifiers
Linear Discriminant Analysis (LDA)
Binary Classifier with PCA
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
Conclusions to Section 9
Section 10 : 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
Section 11 : 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
Section 12 : 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
Section 13 : 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
Tweet Sentiments- Mugabe's Ouster
Examine the Polarity of Text
Polarity of Individual Tweets
Topic Modelling a Document
Topic Modelling Multiple Documents
Conclusions to Section 13
Section 14 : Text Data and Machine Learning
EDA With Text Data
Identify Deceptive Reviews With Supervised Classification
Identify Spam Emails with Supervised Classification
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