Data Science: Data Mining & Natural Language Processing in R

  Section 1 : INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
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Introduction Preview
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Data and Scripts For the Course
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Introduction to R and RStudio
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Start with Rattle
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Conclusion to Section 1

  Section 2 : Reading in Data from Different Sources in R
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Read in Data from CSV and Excel Files Preview
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Read Data from a Database
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Read Data from JSON
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Read in Data from Online CSVs
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Read in Data from Online HTML Tables-Part 1
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Read in Data from Online HTML Tables-Part 2
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Read Data from Other Sources
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Conclusions to Section 2

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

  Section 4 : Data Mining for Patterns and Relationships
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What is Data Mining?
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Association Mining with Apriori
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Apriori with Real Data
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Visualize the Rules
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Association Mining with Eclat Preview
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Eclat with Real Data

  Section 5 : Machine Learning for Data Science
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How is Machine Learning Different from Statistical Data Analysis?
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What is Machine Learning (ML) About? Some Theoretical Pointers

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

  Section 7 : Dimension Reduction
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Dimensionality Reduction-theory
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PCA
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Removing Highly Correlated Predictor Variables
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Variable Selection Using LASSO Regression
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ariable Selection With FSelector
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Boruta Analysis for Feature Selection
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Conclusions to Section 7

  Section 8 : Supervised Learning Theory
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Some Basic Supervised Learning Concepts
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Pre-processing for Supervised Learning

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

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

  Section 11 : Introduction to Artificial Neural Networks (ANN)
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What are Artificial Neural Networks?
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Neural Network for Binary Classifications
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Neural Network with PCA for Binary Classifications
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Neural Network for Regression
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More on Neural Networks- with neuralnet
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Identify Variable Importance in Neural Networks

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

  Section 13 : Gaining Insights from Text Data- Text Mining and Natural Language Processing (NL
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Explore Tweet Data
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Visualize Tweet Sentiment Wordcloud- India's Demonetization Policy
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More Wordclouds: Amazon Review Data
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Word Frequency in Text Data
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Tweet Sentiments- India's Demonetization Policy
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Tweet Sentiments- Mugabe's Ouster
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Examine the Polarity of Text
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Polarity of Individual Tweets
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Topic Modelling a Document
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Topic Modelling Multiple Documents
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Conclusions to Section 13

  Section 14 : Text Data and Machine Learning
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EDA With Text Data
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Identify Deceptive Reviews With Supervised Classification
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Identify Spam Emails with Supervised Classification