This post will cover association rules and k-means clustering for identifying associations among items in transactional data. I will introduce the start-to-finish steps needed for using association rules to perform a market basket analysis on real-world data.
Now I start to learn neural networks and SVM. Actually, I am a little bit more familiar with those two parts than other machine learning methods. However, the mathematics behind SVM or DNN is some kind of complicated, I will skip the details in the post.
As time goes by, I go to the part about regression. In the meanwhile, I am watching Andrew Ng’s Machine Learning online course, which also covers regression. I read the book like facing a black box but very useful. Andrew’s video tells me why, and I can look into the methods more deeply.
In this blog, I will introduce three main machine learning models and their implementation in R: K-Nearest Neighbors, Naive Bayes, and Decision tree. I am not going to go into those methods seriously now, but I hope I will have a better understanding of those methods after I watch Android Ng’s online course and read Zhihua Zhou’s book.
In the following series of blogs, I will make some notes about the book “Machine Learning with R” written by Brett Lantz. In this blog, I will start with the introduction of R and Data Management.