• Machine Learning in R

  • Group Training

    Machine Learning in R goes through the basics of coding, cleaning data, analyzing data, visualizing data, and modeling data.

    Training code
    CGAMLINRCD
    Spoken Language
    Dutch
    Language Materials
    English
    Dayparts
    6
    Price
    €2.250,00
    excl. VAT No extra costs.

    Book Machine Learning in R now

    In group training, we use several learning methods to help you obtain the knowledge, give you helpful insights and get you inspired. Check the Spoken language and Language materials on the left for language info.

    This course currently isn't planned. Please fill in your contact details below and we'll get in touch with you within two working days.

    Name*
    Email Address*
    Phone number*
     

    What is Machine Learning in R

    Machine Learning in R goes through the basics of coding, cleaning data, analyzing data, visualizing data, and modeling data. In a bird's eye view of 3 days you will be taken through the different steps of a data pipeline. In the workshop you will receive theoretical information, but the emphasis will be placed on practice. You practice in R with datasets with various themes.
    Machine Learning in R teaches you how to deal with raw data, analyze it and make the most of it by going through a pipeline. Furthermore, this training also focuses on how to communicate findings to the business.
    You will be introduced to topics such as:

    • Data types
    • Data visualization
    • Tidyverse
    • Cross-validation
    • Balancing data
    • Data wrangling
    • Supervised machine learning (logistic regression, random forest)
    • Unsupervised machine learning (k-means clustering)
    • Confusion matrix
    • Creating an analysis report in R with Rmarkdown
     
     

    Who should attend Machine Learning in R

    The Machine Learning in R training is suitable for:

    • People with an interest in applying R for Data Analysis
    • People who work in Data Analysis and want to take a first step towards Data Science
    • People who want to start programming in R
    • People who want to know how to build pipeline to turn raw data into findings

    Prerequisites

    Beginning skills and general knowledge of data analysis. The 'Introduction to Data Analysis' course addresses these topics and is ideally suited to follow as a training course prior to this.

    Objectives

    After this training, the participant is able to answer the questions:

    • Which steps can I go through to answer a practical question with data analysis and data science?
    • How do I clean my data with R and what things should I pay attention to when I clean my data?
    • How do I use the R programming language?
    • Which packages can I use to analyze my data?
     
    Incompany

    Machine Learning in R goes through the basics of coding, cleaning data, analyzing data, visualizing data, and modeling data.

    Training code
    CGAMLINRCD
    Spoken Language
    Dutch
    Language Materials
    English
    Dayparts
    6
    Price
    €2.250,00
    excl. VAT No extra costs.

    With an Incompany training you have several advantages:

    - You choose the location
    - You experience the training with your colleagues, so it is always in line with your practice
    - The trainer can tailor explanations, examples and assignments to your organization
    - In consultation exercises can be adapted to organization-specific questions

    Request more information or a quote.

     

    What is Machine Learning in R

    Machine Learning in R goes through the basics of coding, cleaning data, analyzing data, visualizing data, and modeling data. In a bird's eye view of 3 days you will be taken through the different steps of a data pipeline. In the workshop you will receive theoretical information, but the emphasis will be placed on practice. You practice in R with datasets with various themes.
    Machine Learning in R teaches you how to deal with raw data, analyze it and make the most of it by going through a pipeline. Furthermore, this training also focuses on how to communicate findings to the business.
    You will be introduced to topics such as:

    • Data types
    • Data visualization
    • Tidyverse
    • Cross-validation
    • Balancing data
    • Data wrangling
    • Supervised machine learning (logistic regression, random forest)
    • Unsupervised machine learning (k-means clustering)
    • Confusion matrix
    • Creating an analysis report in R with Rmarkdown
     
     

    Who should attend Machine Learning in R

    The Machine Learning in R training is suitable for:

    • People with an interest in applying R for Data Analysis
    • People who work in Data Analysis and want to take a first step towards Data Science
    • People who want to start programming in R
    • People who want to know how to build pipeline to turn raw data into findings

    Prerequisites

    Beginning skills and general knowledge of data analysis. The 'Introduction to Data Analysis' course addresses these topics and is ideally suited to follow as a training course prior to this.

    Objectives

    After this training, the participant is able to answer the questions:

    • Which steps can I go through to answer a practical question with data analysis and data science?
    • How do I clean my data with R and what things should I pay attention to when I clean my data?
    • How do I use the R programming language?
    • Which packages can I use to analyze my data?
     
  • Related

    Fields of Expertise
    Data Analytics
     
  • e-CF competences with this course

     

    At Capgemini Academy we believe in transparency and clarity in the training landscape. That is why, in the table below, we show you to which e-CF competence this training or certification contributes. For more information about how to use the e-Competence Framework read more here. If you want to know how you can apply the e-CF within your organization, read more on this page.

    e-Competence Level12345
    A.6.Application Design     
    D.10.Information and Knowledge Management     
    B.1.Application Development     
    E.1.Forecast Development