Data mining

Introduction to R:

  • Installing R
  • Installing R Studio
  • Creating Objects in R
  • Creating Arrays
  • Creating Data frames
  • Use of Structure
  • Dimensions
  • Loading CSv files, Foreign packages into R
  • Data Manipulation with R:
  • Loading vectors in R
  • Combining to vectors in R
  • Cleaning Data with R, Swapping Data, Sorting Data, Converting unstructured to structured data, usage of sub, gsub, regexpr, gregexpr, apply, lapply, sapply
  • Data Visualization with R:
  • Usage of Plot, lines, boxplot, stars, barplot, pie, hist, rug, sunflowerplot, various color of histograms, tabplot, ggplot2, maptools and extracting data from URLs

 “Machine Leaning”

  • Foundation:
  • Machine Learning Introduction: Supervised and Unsupervised Learning
  • Linear Regression Theory
  • Linear Regression Programming with R
  • Working on Case Study
  • Multiple Linear Regression
  • Theory behind multiple linear regression
  • Multiple Linear Regression with R
  • Working on Case Study
  • Decision Tree: Theory Behind Decision Tree
  • Decision Tree with R
  • Working on Case Study

Naive Bayes:

  • Theory behind Naïve Bayes classifiers
  • Naive Bayes Classifiers with R
  • Working on Case Study

Support Vector Machines:

  • Theory behind Support Vector Machines
  • Support vector machines with R
  • Improving the performance with Kernals
  • Working on Case Study

Association Rule:

  • Theory behind Association Rule
  • Working on Case Studies
  • Expert:
  • Neural Net:

Artificial Neural Network

  • Connection Weights in Neural Network
  • Generating Neural Network with R
  • Improving Neural Network Accuracy with Hidden Layers
  • Working on Case
  • Random Forest:

Theory behind Random Forest

  • Random Forest with R
  • Improving performance of Random Forest
  • Working on Case Study
  • Recommendation Engine:

Theory behind Recommendation Engines

  • Working on Case Study with R
  • Dimension Reduction:
  • Theory behind Recommendation Engine
  • Working on Case Studies

 

Text Mining:

Introduction to Text Mining concepts

  • Sentiment Analysis with R/li>
  • Positive and Negative Word Cloud
  • Case study on Sentiment analysis
  • Advanced Regression
  • Theory Behind Advanced Regression
  • Advanced Regression with R
  • Working on Case Study
  • Web Analytics:
  • Theory behind Web Analytics
  • Working on Case Study

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