What you'll get
- 25+ Hours
- 3 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Learn the fundamentals of machine learning in R, including data preprocessing, wrangling, and model building.
- Learn supervised learning techniques, including regression and classification, using R.
- Apply machine learning concepts to real-world projects using the Caret package in R.
- Understand model evaluation, optimization, and performance assessment in R.
- Leverage R for hands-on data science applications.
- Import and manage data from various sources into the R environment.
- Perform data preprocessing and wrangling efficiently in RStudio.
- Implement unsupervised learning techniques, including k-means clustering.
- Apply dimensionality reduction techniques, like PCA, and perform feature selection.
- Utilize supervised learning algorithms like Random Forests for classification tasks.
- Evaluate model performance effectively and learn best practices for assessing machine learning model accuracy.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning with R | 20h 25m | ✔ | View Curriculum |
| Machine Learning with R 2022 | 3h 05m | ✔ | View Curriculum |
| Machine Learning Project in Python | 1h 58m | ✔ | View Curriculum |
Description
This course provides participants with practical expertise and a solid understanding of machine learning using R. The curriculum divides into three sections: fundamentals, supervised learning techniques, and a hands-on project using the Caret package. Participants learn to apply machine learning algorithms to real-world datasets, evaluate model performance, and develop predictive models.
Section 1: Machine Learning with R
Introduces key concepts, data preprocessing, model building, and evaluation methods. Participants gain hands-on experience implementing algorithms and analyzing datasets in R.
Section 2: Supervised Machine Learning with R
Covers regression and classification techniques, including linear and logistic regression, random forests, decision trees, and support vector machines. The course emphasizes feature selection, model tuning, and performance evaluation.
Section 3: Machine Learning Project using Caret in R
Applies skills to a practical project, guiding participants through data preprocessing, algorithm selection, model tuning, and evaluation using cross-validation. Participants create predictive models for real-world applications.
Throughout the course, lectures, demonstrations, and hands-on projects reinforce learning, enabling participants to confidently analyze data and implement machine learning solutions in R.
Requirements
- No prior knowledge required; just a passion for learning.
- Basic computer literacy.
- Willingness to explore new tools and technologies.
Target Audience
- Beginners interested in learning R.
- Data science practitioners aiming to enhance their R and machine learning skills.
- Developers seeking to explore various aspects of machine learning.
