Practical Machine Learning in R Description
Practical Machine Learning in R by Fred Nwanganga is not just an ordinary plain book about technical stuff. It is regarded as a perfect guide for self-learners taking on the machine-learning field with a remarkable amount of vivid real-life illustrations of machine-learning uses in worldwide businesses.
You will be under the guidance of the data analytics expert Fred Nwanganga and his companions clearly explaining everything you need to know about machine learning with hands-on examples demonstrated in the R programming language.
Here are what you will learn in this course:
- Explores data management techniques, including data collection, exploration, and dimensionality reduction
- Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat, and clustering
- Describes the principles behind the Nearest Neighbor, Decision Tree, and Naive Bayes classification techniques
- Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost