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R Data Analysis Solutions - Machine Learning Techniques

Video Description

Over 40 recipes dedicated to machine learning techniques

About This Video

  • Get data into your R environment and prepare it for analysis

  • Apply several machine-learning techniques for classification and regression

  • Simplify data

  • In Detail

    Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it. This video empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. You will learn to carry out different tasks on the data to bring it into action.By the end of this course, you will be able to carry out different analyzing techniques, apply classification and regression, and also reduce data.

    Table of Contents

    1. Chapter 1 : Acquire and Prepare the Ingredients – Your Data
      1. The Course Overview 00:03:49
      2. Reading Data from CSV Files 00:06:30
      3. Reading XML and JSON Data 00:06:07
      4. Reading Data from Fixed-Width Formatted Files, R Files, and R Libraries 00:06:39
      5. Removing and Replacing Missing Values 00:06:17
      6. Removing Duplicate Cases 00:02:04
      7. Rescaling a Variable 00:02:16
      8. Normalizing or Standardizing Data in a Data Frame 00:03:05
      9. Binning Numerical Data 00:03:27
      10. Creating Dummies for Categorical Variables 00:03:50
    2. Chapter 2 : What's in There? – Exploratory Data Analysis
      1. Creating Standard Data Summaries 00:03:29
      2. Extracting Subset of a Dataset 00:05:45
      3. Splitting a Dataset 00:01:55
      4. Creating Random Data Partitions 00:07:38
      5. Generating Standard Plots 00:05:24
      6. Generating Multiple Plots 00:01:49
      7. Selecting a Graphics Device 00:01:53
      8. Creating Plots with the Lattice and ggplot2package 00:09:05
      9. Creating Charts that Facilitate Comparisons 00:02:44
      10. Creating Charts that Visualize Possible Causality 00:01:36
      11. Creating Multivariate Plots 00:02:14
    3. Chapter 3 : Where Does It Belong? – Classification
      1. Generating Error/Classification-Confusion Matrices 00:04:26
      2. Generating ROC Charts 00:03:48
      3. Building, Plotting, and Evaluating – Classification Trees 00:06:07
      4. Using random Forest Models for Classification 00:04:21
      5. Classifying Using the Support Vector Machine Approach 00:05:27
      6. Classifying Using the Naïve Bayes Approach 00:02:22
      7. Classifying Using the KNN Approach 00:05:03
      8. Using Neural Networks for Classification 00:04:18
      9. Classifying Using Linear Discriminant Function Analysis 00:02:49
      10. Classifying Using Logistic Regression 00:04:02
      11. Using AdaBoost to Combine Classification Tree Models 00:03:32
    4. Chapter 4 : Give Me a Number – Regression
      1. Computing the Root Mean Squared Error 00:02:44
      2. Building KNN Models for Regression 00:08:23
      3. Performing Linear Regression 00:07:17
      4. Performing Variable Selection in Linear Regression 00:02:22
      5. Building Regression Trees 00:07:56
      6. Building Random Forest Models for Regression 00:05:07
      7. Using Neural Networks for Regression 00:03:35
      8. Performing k-Fold Cross-Validation and Leave-One-Out-Cross-Validation 00:05:07
    5. Chapter 5 : Can You Simplify That? – Data Reduction Techniques
      1. Performing Cluster Analysis Using K-Means Clustering 00:06:49
      2. Performing Cluster Analysis Using Hierarchical Clustering 00:04:00
      3. Reducing Dimensionality with Principal Component Analysis 00:04:37