In Machine Learning With R training course, you will start by organizing data and then predicting it with the help of various examples. Explore R Studio and libraries, how to apply linear regression, how to score test sets, and plotting test results on a Cartesian plane. You will also use the caret package in R to simplify some of the classification steps. You will also learn hyper-parameter turning, deep learning, and putting models into production through solid, real-world examples.
By attending Machine Learning With R workshop, delegates will learn to:
- Harness the power of R to build common machine learning algorithms with real-world data science applications
- Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
- Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
- Classify your data with Bayesian and nearest neighbour methods
- Predict values by using R to build decision trees, rules, and support vector machines
- Forecast numeric values with linear regression, and model your data with neural networks
- Understand why and how to create test and training data sets for analysis
- Get to know hyper-parameter tuning by exploring and iterating through parameters
- Classify your data with Bayesian and nearest neighbour methods
- Predict values by using R to build decision trees, rules, and support vector machines
- Forecast numeric values with linear regression, and model your data with neural networks
- Specialized machine learning techniques for text mining, social network data, big data, and more
This Machine Learning With R course is for developers who wish to learn about the ML techniques and implement them in R. Knowledge of R is assumed.