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An Introduction to Statistical Learning with Applications in R

Decoding vast and complex data has never been easier. Level up your data game with R programming.

(STATS-R.AU1) / ISBN : 978-1-64459-616-6
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Lab
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About This Course

Data and statistics are powering business innovations today. Become an indispensable part of this data-driven industry with our online course ‘Statistical Learning with R’. 

Learn how to extract meaningful insights from the most complex and vast datasets. The syllabus covers everything from the fundamental concepts of statistical learning with R to building predictive models and decision-making. 

Grain hands-on experience by decoding complicated data problems with our hands-on lab activities using R programming language. 

Skills You’ll Get

  • Understanding of fundamental statistical concepts like regression, classification, clustering, and dimensionality reduction.
  • Use model assessment techniques like bias-variance trade-off, cross-validation
  • Awareness of hypothesis testing and statistical significance
  • Expertise in R programming for data manipulation, analysis, and visualization
  • Expertise in data cleaning, preprocessing, and analysis
  • Skilled in model building and evaluation by using various statistical methods
  • Ability to interpret model results and make data-driven decisions
  • Mastery in identifying relevant data and extracting meaningful insights
  • Problem-solving mindset for evaluating data analysis results and their implications
  • Skilled in presenting data visualizations and model results clearly
Download Course Outline

1

Preface

2

Introduction

  • An Overview of Statistical Learning
  • A Brief History of Statistical Learning
  • This Course
  • Who Should Read This Course?
  • Notation and Simple Matrix Algebra
  • Organization of This Course
  • Data Sets Used in Labs and Exercises
3

Statistical Learning

  • What Is Statistical Learning?
  • Assessing Model Accuracy
  • Lab: Introduction to R
  • Exercises
4

Linear Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Other Considerations in the Regression Model
  • The Marketing Plan
  • Comparison of Linear Regression with K-Nearest Neighbors
  • Lab: Linear Regression
  • Exercises
5

Classification

  • An Overview of Classification
  • Why Not Linear Regression?
  • Logistic Regression
  • Generative Models for Classification
  • A Comparison of Classification Methods
  • Generalized Linear Models
  • Lab: Classification Methods
  • Exercises
6

Resampling Methods

  • Cross-Validation
  • The Bootstrap
  • Lab: Cross-Validation and the Bootstrap
  • Exercises
7

Linear Model Selection and Regularization

  • Subset Selection
  • Shrinkage Methods
  • Dimension Reduction Methods
  • Considerations in High Dimensions
  • Lab: Linear Models and Regularization Methods
  • Exercises
8

Moving Beyond Linearity

  • Polynomial Regression
  • Step Functions
  • Basis Functions
  • Regression Splines
  • Smoothing Splines
  • Local Regression
  • Generalized Additive Models
  • Lab: Non-linear Modeling
  • Exercises
9

Tree-Based Methods

  • The Basics of Decision Trees
  • Bagging, Random Forests, Boosting, and Bayesian Additive Regression Trees
  • Lab: Decision Trees
  • Exercises
10

Support Vector Machines

  • Maximal Margin Classifier
  • Support Vector Classifiers
  • Support Vector Machines
  • SVMs with More than Two Classes
  • Relationship to Logistic Regression
  • Lab: Support Vector Machines
  • Exercises
11

Deep Learning

  • Single Layer Neural Networks
  • Multilayer Neural Networks
  • Convolutional Neural Networks
  • Document Classification
  • Recurrent Neural Networks
  • When to Use Deep Learning
  • Fitting a Neural Network
  • Interpolation and Double Descent
  • Lab: Deep Learning
  • Exercises
12

Survival Analysis and Censored Data

  • Survival and Censoring Times
  • A Closer Look at Censoring
  • The Kaplan-Meier Survival Curve
  • The Log-Rank Test
  • Regression Models With a Survival Response
  • Shrinkage for the Cox Model
  • Additional Topics
  • Lab: Survival Analysis
  • Exercises
13

Unsupervised Learning

  • The Challenge of Unsupervised Learning
  • Principal Components Analysis
  • Missing Values and Matrix Completion
  • Clustering Methods
  • Lab: Unsupervised Learning
  • Exercises 
14

Multiple Testing

  • A Quick Review of Hypothesis Testing
  • The Challenge of Multiple Testing
  • The Family-Wise Error Rate
  • The False Discovery Rate
  • A Re-Sampling Approach to p-Values and False Discovery Rates
  • Lab: Multiple Testing
  • Exercises 

1

Introduction

  • Analyzing Stock Market Trends Using the Smarket Dataset from ISLR
  • Analyzing Wage Data Using the ISLR Package
2

Statistical Learning

  • Implementing the Bayes Classifier
  • Implementing the Bias-Variance Trade-Off
  • Indexing Data
3

Linear Regression

  • Implementing Simple Linear Regression
  • Performing Multiple Linear Regression
  • Implementing Qualitative Predictors Using the Credit Dataset from ISLR
  • Implementing Non-linear Transformations of Predictors
4

Classification

  • Implementing Multinomial Logistic Regression
  • Implementing Multiple Logistic Regression
  • Implementing Naive Bayes Classification
  • Implementing Quadratic Discriminant Analysis
  • Generating and Visualizing Multivariate Gaussian Distribution
  • Implementing Linear Discriminant Analysis
  • Implementing the Generalized Linear Model
  • Implementing Poisson Regression
  • Implementing K-Nearest Neighbors on the Caravan Dataset from ISLR
5

Resampling Methods

  • Implementing the Validation Set Approach with the Auto Dataset from ISLR
  • Implementing Leave-One-Out Cross-Validation
  • Implementing K-Fold Cross-Validation
  • Understanding Bootstrapping Techniques on the Portfolio Dataset from ISLR
6

Linear Model Selection and Regularization

  • Implementing Subset Selection Methods Using the Hitters Dataset from ISLR
  • Implementing Forward and Backward Stepwise Selection
  • Implementing Lasso Regression
  • Implementing Ridge Regression
  • Implementing Partial Least Squares
  • Improving Predictions with Principal Components Regression
7

Moving Beyond Linearity

  • Implementing Polynomial Regression
  • Implementing Step Functions
  • Implementing Splines
  • Improving Generalized Additive Models
8

Tree-Based Methods

  • Implementing Bagging and Random Forests
  • Fitting Regression Trees
  • Improving Model Performance Using Boosting
  • Building and Analyzing Classification Trees Using the Carseats Dataset from ISLR
9

Support Vector Machines

  • Implementing the Maximal Margin Classifier
  • Introducing ROC Curves
  • Implementing Support Vector Classifier
  • Implementing SVM with Multiple Classes
10

Deep Learning

  • Implementing RNN for Time Series Prediction
  • Creating an Image Classifier Using CNNs
  • Implementing RNN for Time Series Prediction
11

Survival Analysis and Censored Data

  • Implementing the Kaplan-Meier Survival Curve
  • Applying the Log-Rank Test
  • Incorporating Shrinkage Techniques into the Cox Model
12

Unsupervised Learning

  • Implementing a Dendrogram
  • Implementing K-Means Clustering
  • Analyzing the NCI60 Data using the ISLR Package
13

Multiple Testing

  • Implementing Family-Wise Error Rate
  • Implementing Holm's Step-Down Procedure
  • Implementing the Benjamini-Hochberg Procedure
  • Implementing the False Discovery Rate

Any questions?
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Know more about our ‘Statistical Learning with Applications in R’ course here.

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Statistical Learning is the study of using statistical methods for analyzing data to extract valuable insights and making data-driven decisions. The key aspects include predictive modeling, pattern recognition, decision making, and data mining.

  All those wanting to learn how to utilize data for driving business growth, should enroll for this course. It will be of great benefit to the following people:

  • Data Scientists
  • Machine Learning Engineers
  • Statisticians
  • Analysts
  • Students and Researchers

No, there’s no need for prior programming knowledge. You’ll be learning it with this course.

Yes, it covers several advanced topics like the following:

  • Understanding of survival analysis, time series analysis, and unsupervised learning
  • Deep learning techniques and their applications
  • Handling complex data structures and performing high-dimensional analysis

It can be effectively used for a wide range of applications including:

  • Predictive analysis
  • Risk assessment
  • Portfolio optimization
  • Fraud detection
  • Customer & market segmentation
  • Climate modeling & species distribution modeling
  • Environment impact assessment
  • Image and speech recognition
  • Anomaly detection
  • Time series analysis

No, advanced maths is not required for using statistical learning techniques.

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