Machine Learning with R

In the course Machine Learning with R you will learn how to apply the R language and the R libraries in modeling projects and machine learning.

Review R

First of all, a review discusses the fundamentals of R such as data types and functions. Then a number of important libraries such as dplyr and ggplot2 are treated.

Machine Learning

Next the principles of machine learning, building models based on data and the differences between supervised and unsupervised learning are explained.

Regressions

Linear regression and logistic regression and the differences between them are discussed. Then attention is paid to how models can be checked for accuracy by looking at summaries, coefficients and plots.

Functional R

Subsequently the course covers how functional programming techniques in R can be applied. Here other solutions for iteration through various map and other functions are discussed.

Sparklyr Intro

Attention is also paid to the access of Apache Spark from R by means of a distributed data frame implementation with operations such as selection, filtering and aggregation.

Shiny

Visualization of data in interactive web applications directly from R via the Shiny package is also on the program.

Decision Trees

Next the course Machine Learning with R discusses Decision Trees. This Machine Learning algorithm is based on classification.

Other Algorithms

Finally the course ends with the discussion of various other Machine Learning algorithms such as Naive Bayes, Principal Component Analysis and Support Vector Machines.

Audience Course Machine Learning with R

The course Machine Learning with R is intended for data analists and data scientists who want to use the R libraries for modeling and machine learning.

Prerequisites training Machine Learning with R

To participate in this course knowledge and experience with the programming language R for Data Analysis is required. Prior knowledge with regard to statistical methods and algorithms is beneficial for the understanding.

Realization course Machine Learning with R

The theory is treated on the basis of presentations. Illustrative demos clarify the concepts. The theory is interspersed with exercises and case studies. The course times are from 9.30 to 16.30.

Official Certificate Machine Learning with R

Participants receive an official Machine Learning with R certificate after successful completion of the course.

Modules

Module 1 : R Review

• R Data Types
• Data Frames
• Factors
• Rmarkdown
• tidy package
• Functions in R
• Apply functions
• Statistics
• R Data Files
• Using dplyr Package
• Plotting with ggplot2

Module 2 : Machine Learning

• What is Machine Learning?
• Building Models of Data
• Model Based Learning
• Tunable Parameters
• Supervised Learning
• Discrete Labels
• Continuous Labels
• Classification and Regression
• Unsupervised Learning
• Data Speaks for Itself
• Clustering and Dimensionality Reduction

Module 3 : Linear Regression

• Check Model
• Using Summary
• Using Coefficients
• Correlation R
• R Squared
• F Test
• Check Model Graphically
• Check Residuals
• Polynomial Regression
• Gaussian Basis Functions
• Overfitting

Module 4 : Logistic Regression

• Compare with Linear Regression
• Explore with Graphics
• Logistic Function
• Checking Model
• Using Summary
• Using Coefficients
• Calculate Probabilities
• Making Predictions
• Confusion Matrix
• Accuracy
• Precision and Recall
• ROC Curve

Module 5 : Functional R

• Solving Iteration
• purr package
• library tidyverse
• map Functions
• Parameters of map
• .x as placeholder
• map_lgl Function
• map_int and map_char
• map2 Function
• Other iteration functions
• Combine purr with dyplr
• walk Function

Module 6 : Sparklyr Intro

• Web Applications
• Shiny Architecture
• Shiny Server
• UI and Server
• Input Object
• Output Object
• Reactivity
• Render Options
• Shiny Functions
• Shiny Layout and Dashboard
• Shiny Performance

Module 7 : Shiny

• Ensemble Learner
• Creating Decision Trees
• DecisionTreeClassifier
• Overfitting Decision Trees
• Ensembles of Estimator
• Random Forests
• Parallel Estimators
• Bagging Classifier
• Random Forest Regression
• RandomForestRegressor
• Non Parametric Model

Module 8 : Decision Trees

• Naive Bayes Classifiers
• Gaussian Naive Bayes
• Principal Component Analysis
• Least Squares
• Polynomial Fitting
• Constrained Linear Regression
• K-Means Clustering
• Support Vector Machines
• Conditional Random Fields
• Explained Variance
• Dimensionality Reduction

Module 9 : Other Algorithms