Objective
This course is technical in nature. It makes use of coding in R and covers the application of machine learning in business.
You will learn:
The fundamental of machine learning techniques that can be applied in a wide range of applications (e.g., finance, banking, real state, transportation/mobility, environmental monitoring, and agriculture, to cite a few).
Certficate, diploma
Certificate of attendance if 80% of class attendance
Target group
Who is the course aimed at?
Students and Recent Graduates
Early- and Mid-Career Professionals
Marketing and Project Management Professionals
Prerequisites
CR studio software is recommended but not mandatory to enable programming directly in a software-based interface.
Section 1: Statistics with R
- Introduction to Statistics with R
- Probability
- Random variables
- Statistical Inference
- Statistical models
- Regression
- Linear Models
- Association is not causation
Section 2: Data Wrangling
- Introduction to Data Wrangling
- Reshaping data
- Joining tables
- Web Scraping
- String Processing
- Parsing Dates and Times
- Text mining
Section 3: Machine Learning
- Introduction to Machine Learning
- Smoothing
- Cross validation
- The caret package
- Examples of algorithms
- Machine learning in practice
- Large datasets
- Clustering and classification
Section 4: Productivity tools
- Introduction to productivity tools
- Organizing with Unix
- Git and GitHub
- Reproducible projects with RStudio and R markdown
Times:
Frequency:
2 x 2 hours per week or dates and time to be determined with participants
Deadline:
Subscribtion condition:
Paiement des droits d'inscription une semaine avant le début des cours
Terms & condition payment:
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