Machine Learning With R
Instructor-led training in Machine Learning With R. Delivered live online by senior practitioners, with hands-on labs and a final assessment. Includes an Skilvi course-completion certificate.
Overview
In the Machine Learning With R course, you will explore the fundamental concepts of machine learning using the R programming language. This course is designed to equip you with the skills necessary to analyze data, build predictive models, and apply machine learning techniques to real-world problems, making it essential for data professionals seeking to enhance their analytical capabilities.
What you'll learn
- You will be able to understand and apply key machine learning concepts and techniques.
- You will be able to preprocess and clean data for analysis in R.
- You will be able to implement supervised learning algorithms, including regression and classification.
- You will be able to use unsupervised learning methods such as clustering and dimensionality reduction.
- You will be able to evaluate model performance using appropriate metrics.
- You will be able to visualize data and model results effectively.
- You will be able to apply machine learning techniques to solve real-world business problems.
Curriculum
7 modules · outline is indicative and can be tailored to your team.
1Introduction to Machine Learning
- Understanding machine learning types: supervised, unsupervised, and reinforcement learning
- The machine learning workflow
- Overview of R and its ecosystem for machine learning
2Data Preparation and Cleaning
- Data import and export techniques in R
- Handling missing values and outliers
- Data normalization and transformation methods
3Supervised Learning Algorithms
- Linear regression and its applications
- Logistic regression for binary classification
- Decision trees and random forests
4Unsupervised Learning Techniques
- Clustering methods: k-means and hierarchical clustering
- Dimensionality reduction techniques: PCA and t-SNE
- Evaluating clustering results
5Model Evaluation and Optimization
- Cross-validation techniques
- Performance metrics for regression and classification
- Hyperparameter tuning and model selection
6Data Visualization
- Creating effective visualizations using ggplot2
- Visualizing model results and feature importance
- Best practices for data storytelling
7Real-World Applications of Machine Learning
- Case studies in various industries
- Building a machine learning project from start to finish
- Ethical considerations in machine learning
Prerequisites
Some familiarity with R programming is recommended.
Who should attend
This course is ideal for data analysts, data scientists, and professionals looking to deepen their machine learning skills.
Certification
Frequently asked questions
What is the format of the course delivery?
The course is instructor-led and delivered live online.
How long is the course duration?
The course spans 6 weeks, with one session per week, each lasting 2 hours.
Will I receive a certificate upon completion?
Yes, you will receive an Skilvi course-completion certificate.
Are exam vouchers provided?
Exam vouchers are available through authorized channels on request.
What are the prerequisites for this course?
Some familiarity with R programming is recommended.
Pricing on request
- Live online (VILT)
- 24–32 hours
- Hands-on labs & assignments
- Skilvi completion certificate
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