I am super excited to announce my 3rd book as a co-author.
What is this book about?
Supervised Learning with R covers the complete process of developing applications with 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 book, you will find yourself an expert at modelling a supervised machine learning algorithm that precisely fulfills your business need.
What will you 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, an R package
- Improve the model performance with feature selection and dimensionality reduction
- Use a research paper with a real-world problem, frame a problem statement, and replicate its results with a supervised machine learning model
- Study the supervised learning algorithms by using them on real world datasets
- Fine tune optimal parameters with hyperparameter optimization
- Select the best algorithm using the model evaluation framework