Machine Learning in R (NL)
The course is not scheduled in our open calendar. Please fill in your details below and we will contact you within 2 working days.
"*" indicates required fields
What is Machine Learning in R
Machine Learning in R covers the essentials of coding, data cleaning, analysis, visualization, and modelling in R. Over three days, you’ll explore the steps of a data pipeline. The workshop combines theoretical knowledge with hands-on practice using diverse datasets in R. You’ll learn to handle raw data, analyze it, and communicate your findings effectively. Topics include data types, visualization, Tidyverse, cross-validation, data balancing, wrangling, supervised learning (logistic regression, random forest), unsupervised learning (k-means clustering), confusion matrix, and creating reports in R with Rmarkdown.
Machine Learning in R combines theoretical knowledge with practical application, led by expert trainers who share real-world insights and best practices. This course empowers you to handle and analyze data proficiently, making you adept at using R for a variety of machine learning tasks.
Who should attend Machine Learning in R
- Data Analysts: Enhance your data analysis skills with machine learning techniques in R.
- Data Scientists: Deepen your understanding of machine learning algorithms and their applications using R.
- Statisticians: Apply statistical methods to real-world data using R.
- Researchers: Utilize R for advanced data analysis and modeling in your research projects.
- IT Professionals: Gain proficiency in R for data-driven decision-making.
- Business Analysts: Learn to derive actionable insights from data through machine learning using R.
- Entrepreneurs: Leverage data analysis and machine learning to drive business growth.
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.
During this training you need a laptop on which you can install software: R.
Objectives
At the end of the training you will be able to:
- Identify the steps to answer practical questions with data analysis and data science.
- Clean data in R and understand key considerations in the cleaning process.
- Utilize the R programming language effectively.
- Apply various R packages for data analysis.
e-CF competences with this course
- A.6. Application Design
- B.1. Application Development
- D.10. Information and Knowledge Management
- E.1. Forecast Development
Classroom, online, blended and in-company
At Capgemini Academy you learn in the way that suits you. Do you prefer classroom training, online or a combination of the two (blended)? You can follow most training courses in-company: within your own organization. We use a variety of tools to make learning even more fun and effective. Consider videos, games, quizzes, webinars and case studies, for example. And you can always contact your trainer with any questions.
In-company training courses
With an in-company training you have several advantages:
- You choose the location.
- You train with your colleagues, ensuring it aligns with your practice.
- The trainer tailors explanations, examples and assignments to your organization.
- In consultation, exercises can be adapted to organization-specific questions.
Request more information or a quote.
