Machine Learning with R

All times GMT (UTC)

10am
Introduction to Machine Learning Concepts and Data Preprocessing 

• Overview of machine learning concepts and applications 
• Introduction to common machine learning algorithms such as regression, classification, and clustering 
• Exploring data preprocessing techniques including data cleaning, feature scaling, and feature engineering 
• Hands-on exercise(s): implementing data preprocessing techniques in R 

11.15am
Break

11.45am
Supervised Learning Algorithms in R 

• Understanding supervised learning algorithms such as linear regression, logistic regression, decision trees, and random forests 
• Implementing supervised learning algorithms using R packages like caret and randomForest 
• Evaluating model performance using metrics such as accuracy, precision, recall, and F1-score 
• Practical exercise(s): building and evaluating predictive models with real-world datasets

1pm
Lunch break

2pm
Unsupervised Learning and Model Evaluation 

• Exploring unsupervised learning algorithms such as k-means clustering and hierarchical clustering 
• Implementing unsupervised learning algorithms in R for tasks like customer segmentation and anomaly detection 
• Techniques and metrics for evaluating clustering performance 
• Hands-on activity(s): applying unsupervised learning techniques to real-world datasets 

3.15pm
Break

3.45pm
Advanced Topics in Machine Learning with R 

• Overview of advanced machine learning topics such as ensemble learning, dimensionality reduction, and feature selection 
• Implementing advanced machine learning techniques in R using packages like xgboost and caret 
• Strategies for hyperparameter tuning and model optimization 
• Case studies and practical examples: applying advanced machine learning techniques to solve complex problems in various domains 

5pm
End of day