Learning Path: R: Data Analysis And Machine Learning With R

Conquer the wider world of data science with R

What you'll learn

Understand how to organize and set up data

Learn to label and scale data

Use the caret package to apply and score a model

Handle missing values and duplicates

Apply classification and regression techniques

Conduct independent data analysis

Knowthe essentials of ROC curves

Explore multinomial logistic regression with categorical response variables at three levels

Requirements

Working knowledge of R is expected

Basic knowledge of math and statistics is needed

Description

With its popularity as a statistical programming language rapidly increasing with each passing day, R is becoming the preferred tool of choice for data analysts and data scientists who want to make sense of large amounts of data as quickly as possible. R has a rich set of libraries that can be used for basic as well as advanced data analysis and machine learning tasks.
So, if you're looking to understand how the R programming environment and packages can be used to for data analysis and machine learning, then you should surely go for this Learning Path.



Packt’s Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.




This Learning Path starts by organizing the data and then predicting it. You will work through various examples wherein you will explore RStudio and libraries, how to apply linear regression, how to score test sets, and plotting test results on a Cartesian plane. You will also see how to use logistic regression to predict for a classification problem on automobile data. Further, you will learn different ways to use R to generate professional analysis reports. Moving ahead, you will learn various important analysis and machine learning tasks that you can try out with associated and readily available data with the help of examples. Finally, you will learn advanced data analysis concepts such as cluster analysis, time-series analysis, PCA (Principal Component Analysis), sentiment analysis, and spatial data analysis.




By the end of this Learning Path, you will have a solid understanding of how to efficiently perform data analysis and machine learning tasks using R.



About the Author:

For this course, we have combined the best works of these esteemed authors:




Tim Hoolihan currently works at DialogTech, a marketing analytics company focused on conversations. He is the senior director of data science there. Prior to that, he was CTO at Level Seven, a regional consulting company in the US Midwest. He is the organizer of the Cleveland R User Group.In his job, he uses deep neural networks to help automate of lot of conversation classification problems. In addition, he works on some side-projects researching other areas of artificial intelligence and machine learning.ViswaViswanathan is an associate professor of computing and decision sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in Artificial Intelligence,Viswa has taught extensively in diverse fields, including operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to teaching at the university, hehas conducted training programs for industry professionals. He has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education.ShanthiViswanathan is an experienced technologist who has delivered technology management and enterprise architecture consultations to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE, among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling.Dr. Bharatendra Rai is a professor of business statistics and operations management in the Charlton College of Business at UMass Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne State University, Detroit. His two master's degrees include specializations in quality, reliability, and OR from Indian Statistical Institute and another in statistics from Meerut University, India. He teaches courses on topics such as analyzing big data, business analytics,and data mining, Twitter and text analytics, applied decision techniques, operations management, and data science for business. Dr. Rai has won awards for excellence and exemplary teamwork at Ford for his contributions in the area of applied statistics.



Overview

Section 1: Getting Started with Machine Learning with R

Lecture 1 The Course Overview

Lecture 2 Your R Environment

Lecture 3 Exploring the US Arrests Dataset

Lecture 4 Creating Test and Train Datasets

Lecture 5 Creating a Linear Regression Model

Lecture 6 Scoring on the Test Set

Lecture 7 Plotting the Test Results

Lecture 8 EDA: mtcars

Lecture 9 Working with Factors

Lecture 10 Scaling Data

Lecture 11 Creating a Classification Model

Lecture 12 Advanced Formulas

Lecture 13 Precision, Recall, and F-Score

Lecture 14 Introduction to Caret

Lecture 15 EDA and Preprocessing

Lecture 16 Preparing Test and Train Datasets

Lecture 17 Creating a Model

Lecture 18 Cross Validation

Lecture 19 F-Score

Section 2: R Data Analysis Solutions - Machine Learning Techniques

Lecture 20 The Course Overview

Lecture 21 Reading Data from CSV Files

Lecture 22 Reading XML and JSON Data

Lecture 23 Reading Data from Fixed-Width Formatted Files, R Files, and R Libraries

Lecture 24 Removing and Replacing Missing Values

Lecture 25 Removing Duplicate Cases

Lecture 26 Rescaling a Variable

Lecture 27 Normalizing or Standardizing Data in a Data Frame

Lecture 28 Binning Numerical Data

Lecture 29 Creating Dummies for Categorical Variables

Lecture 30 Creating Standard Data Summaries

Lecture 31 Extracting Subset of a Dataset

Lecture 32 Splitting a Dataset

Lecture 33 Creating Random Data Partitions

Lecture 34 Generating Standard Plots

Lecture 35 Generating Multiple Plots

Lecture 36 Selecting a Graphics Device

Lecture 37 Creating Plots with the Lattice and ggplot2package

Lecture 38 Creating Charts that Facilitate Comparisons

Lecture 39 Creating Charts that Visualize Possible Causality

Lecture 40 Creating Multivariate Plots

Lecture 41 Generating Error/Classification-Confusion Matrices

Lecture 42 Generating ROC Charts

Lecture 43 Building, Plotting, and Evaluating – Classification Trees

Lecture 44 Using random Forest Models for Classification

Lecture 45 Classifying Using the Support Vector Machine Approach

Lecture 46 Classifying Using the Naïve Bayes Approach

Lecture 47 Classifying Using the KNN Approach

Lecture 48 Using Neural Networks for Classification

Lecture 49 Classifying Using Linear Discriminant Function Analysis

Lecture 50 Classifying Using Logistic Regression

Lecture 51 Using AdaBoost to Combine Classification Tree Models

Lecture 52 Computing the Root Mean Squared Error

Lecture 53 Building KNN Models for Regression

Lecture 54 Performing Linear Regression

Lecture 55 Performing Variable Selection in Linear Regression

Lecture 56 Building Regression Trees

Lecture 57 Building Random Forest Models for Regression

Lecture 58 Using Neural Networks for Regression

Lecture 59 Performing k-Fold Cross-Validation and Leave-One-Out-Cross-Validation

Lecture 60 Performing Cluster Analysis Using K-Means Clustering

Lecture 61 Performing Cluster Analysis Using Hierarchical Clustering

Lecture 62 Reducing Dimensionality with Principal Component Analysis

Section 3: Mastering Data Analysis with R

Lecture 63 The Course Overview

Lecture 64 Getting Started and Data Exploration with R/RStudio

Lecture 65 Introduction to Visualization

Lecture 66 Interactive Visualization

Lecture 67 Geographic Plots

Lecture 68 Advanced Visualization

Lecture 69 Getting Introductory Concepts

Lecture 70 Data Partitioning with R

Lecture 71 Multiple Linear Regression with R

Lecture 72 Multicollinearity Issues

Lecture 73 Logistic Regression with Categorical Response Variables at two Levels

Lecture 74 Logistic Regression Model and Interpretation

Lecture 75 Misclassification Error and Confusion Matrix

Lecture 76 ROC Curves

Lecture 77 Prediction and Model Assessment

Lecture 78 Multinomial Logistic Regression with Categorical Response Variables at 3Levels

Lecture 79 Multinomial Logistic Regression Model and Its Interpretation

Lecture 80 Misclassification Error and Confusion Matrix

Lecture 81 Prediction and Model Assessment

Lecture 82 Ordinal Logistic Regression with R

Lecture 83 Ordinal Logistic Regression Model and Interpretation

Lecture 84 The Misclassification Error and Confusion Matrix

Lecture 85 Prediction and Model Assessment

This Learning Path is for data scientists and data analysts who want to perform advanced data analysis and machine learning tasksusing R.