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Course Machine Learning with R

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  • Training
  • Modules
  • General
    General
  • Reviews
  • Certificate
  • Course Machine Learning with R : Content

    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. Machine Learning is part of artificial intelligence and concerns the study of algorithms that automatically improve based on experience. Machine learning algorithms create a model based on training data and can then make predictions about new data.

    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.

  • Course Machine Learning with R : Training

    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.

    Machine-Learning-with-R
  • Course Machine Learning with R : Modules

    Module 1 : R Review

    Module 2 : Machine Learning

    Module 3 : Linear Regression

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

    Module 5 : Functional R

    Module 6 : Sparklyr Intro

    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
    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
    Spark Session
    Copy data into Spark
    File Setup
    Load data
    Spark SQL
    Store Data
    Using dplyr
    showquery()
    Spark DataFrame Functions
    sdf_pivot()
    Feature Transformers
    Distributed R

    Module 7 : Shiny

    Module 8 : Decision Trees

    Module 9 : Other Algorithms

    Web Applications
    Shiny Architecture
    Shiny Server
    UI and Server
    Input Object
    Output Object
    Reactivity
    Render Options
    Shiny Functions
    Shiny Layout and Dashboard
    Shiny Performance
    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
    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
  • Course Machine Learning with R : General

    Course Forms

    All our courses are classroom courses in which the students are guided through the material on the basis of an experienced trainer with in-depth material knowledge. Theory is always interspersed with exercises.

    Customization

    We also do custom classes and then adjust the course content to your wishes. On request we will also discuss your practical cases.

    Course times

    The course times are from 9.30 to 16.30. But we are flexible in this. Sometimes people have to bring children to the daycare and other times are more convenient for them. In good consultation we can then agree on different course times.

    Hardware

    We take care of the computers on which the course can be held. The software required for the course has already been installed on these computers. You do not have to bring a laptop to participate in the course. If you prefer to work on your own laptop, you can take it with you if you wish. The required software is then installed at the start of the course.

    Software

    Our courses are generally given with Open Source software such as Eclipse, IntelliJ, Tomcat, Pycharm, Anaconda and Netbeans. You will receive the digital course material to take home after the course.

    Lunch

    The course includes lunch that we use in a restaurant within walking distance of the course room.

    Locations

    The courses are planned at various places in the country. A course takes place at a location if at least 3 people register for that location. If there are registrations for different locations, the course will take place at our main location, Houten which is just below Utrecht. A course at our main location also takes place with 2 registrations and regularly with 1 registration. And we also do courses at the customer’s location if they appreciate that.

    Evaluations

    At the end of each course, participants are requested to evaluate the course in terms of course content, course material, trainer and location. The evaluation form can be found at https://www.klantenvertellen.nl/reviews/1039545/spiraltrain?lang=en. The evaluations of previous participants and previous courses can also be found there.

    Copyright

    The intellectual property rights of the published course content, also referred to as an information sheet, belong to SpiralTrain. It is not allowed to publish the course information, the information sheet, in written or digital form without the explicit permission of SpiralTrain. The course content is to be understood as the description of the course content in sentences as well as the division of the course into modules and topics in the modules.

  • Course Machine Learning with R : Reviews

  • Course Machine Learning with R : Certificate