What you'll get
- 25+ Hours
- 3 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Build a strong foundation in machine learning using R for data-driven applications.
- Perform data import, cleaning, preprocessing, and transformation within the R environment.
- Apply supervised learning methods such as regression and classification
- Implement unsupervised learning techniques, including clustering and dimensionality reduction
- Develop predictive models using real-world datasets with the Caret framework
- Evaluate, tune, and optimize machine learning models for accuracy and reliability
- Gain hands-on experience through practical data science exercises and projects
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning with R | 20h 25m | ✔ | View Curriculum |
| Linear Regression with R | 3h 05m | ✔ | View Curriculum |
| Machine Learning Project using Caret in R | 1h 58m | ✔ | View Curriculum |
Description
The Machine Learning with R course equips learners with practical skills to design, implement, and evaluate machine learning models using the R programming ecosystem. The curriculum blends conceptual understanding with hands-on implementation, enabling participants to work confidently with real-world datasets.
The course progresses through core machine learning concepts, supervised learning techniques, and a project-based module using the Caret package. Learners explore data preprocessing, feature engineering, model training, and performance evaluation while gaining experience with commonly used R algorithms.
By the end of the program, participants will be able to apply machine learning techniques effectively for data science and analytics use cases using R.
Goals
- Establish a solid understanding of machine learning concepts using R
- Enable learners to build and evaluate predictive models independently
- Prepare participants to apply machine learning techniques to real-world data problems
Objectives
- Import, clean, and prepare datasets for machine learning workflows
- Implement supervised learning algorithms such as regression and classification
- Apply unsupervised learning techniques, including clustering and PCA
- Use the Caret package to train, tune, and validate models
- Evaluate model performance using industry-standard metrics and best practices
Highlights
- Hands-on learning with real-world datasets
- Step-by-step implementation of machine learning algorithms in R
- Practical project using the Caret framework
- Coverage of both supervised and unsupervised learning techniques
- Emphasis on model evaluation, optimization, and performance analysis
Requirements
- Basic computer literacy
- Willingness to learn new tools and concepts
- No prior experience in machine learning or R is required
Target Audience
- Beginners looking to start a career in data science using R
- Data analysts seeking to expand into machine learning
- Developers interested in applying machine learning techniques
- Professionals aiming to strengthen their predictive analytics skills
FAQ
Q1. Is prior experience in R required?
No, the course introduces concepts step by step and is suitable for beginners.
Q2. Does the course include practical projects?
Yes, learners work on hands-on exercises and a real-world project using the Caret package.
Q3. What tools are used in the course?
The course primarily uses R, RStudio, and the Caret Package.
Q4. Can this course help with a career in data science?
Yes, the course builds job-relevant machine learning and data analysis skills using R.
Career Benefits
- Enhances employability in data science and analytics roles
- Builds practical experience in machine learning using R
- Strengthens skills in predictive modeling and data-driven decision-making
- Prepares learners for advanced studies or real-world machine learning projects
