Clustering and Classification with Machine Learning in R
The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R
Created by Minerva Singh
Discover how to uncover meaningful patterns in your data using clustering and classification techniques with R. Learn to work with real-world datasets and apply both supervised and unsupervised machine learning methods to solve practical problems. Build confidence in using R for data science tasks from data cleaning to model evaluation.
Packt | Nov 2019 | 462 min
What You Will Learn
You will start by importing and cleaning data in R, then move on to hands-on practice with clustering and classification algorithms. Each step uses real datasets, so you gain practical experience applying these methods. By the end, you will confidently evaluate and interpret your models' results.
Key Features
- Read and preprocess real-world data in R for machine learning projects
- Apply clustering and classification algorithms to uncover insights from data
- Evaluate and improve model performance using best practices in R
Target Audience
Ideal for those with some R experience who want to apply machine learning in real-world scenarios. If you are a data analyst, researcher, or professional aiming to deepen your practical data science skills in R, you will find the content directly relevant to your goals.
