Supervised Learning with R

R provides excellent visualisation features that are essential to explore data before using it in any automated learning.

Supervised Learning with R covers the complete process of using R to develop applications using supervised machine learning algorithms that cater to your business needs. Your learning curve starts with developing your analytical thinking towards creating a problem statement using business inputs or domain research. You will learn many evaluation metrics that compare various algorithms and you can then use these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine tune your set of optimal parameters. To avoid overfitting your model, you will also be shown how to add various regularization terms.

When you complete the course, you will find yourself to be an expert at modeling a supervised machine learning algorithm that precisely fulfills your business need.

What you will learn

• Develop analytical thinking to precisely identify a business problem
• Wrangle data with dplyr, tidyr, and reshape2
• Visualize data with ggplot2
• Validate your supervised machine learning model using k-fold
• Optimize hyperparameters with grid and random search and bayesian optimization
• Deploy your model on AWS Lambda with plumber
• Improve the model’s performance with feature selection and dimensionality reduction

Hardware Requirements

We recommend the following hardware configuration:

• Processor: Intel or AMD 4-core or better
• Memory: 8 GB RAM
• Storage: 20 GB available space

Software Requirements

You'll need the following software installed in advance:

• Operating systems: Windows 7, 8.1, or 10, Ubuntu 14.04 or later, or macOS Sierra or later
• Browser: Google Chrome or Mozilla Firefox
• RStudio
• RStudio Cloud

You'll also need the following software, packages and libraries installed in advance:

• dplyr
• tidyr
• reshape2
• ggplot2