Category: MACHINE LEARNING

Last time, we even learned a hierarchical clustering algorithm. This time, let’s look at an algorithm called DBSCAN (Density-based spatial clustering of applications with noise). DBSCAN is a very useful clustering algorithm, and its main advantage is that you do not need to specify the number of clusters in advance. This algorithm can also find…

We learned about K-means clustering last time. K-means clustering has a limitation that it can be used only when the density of the cluster is constant and the shape of the cluster is simplified. You also have to specify the number of clusters you want to find. Let’s take a look at the clustering algorithm…

Unsupervised learning There are two types of machine learning algorithms: supervised learning and unsupervised learning. Unsupervised learning refers to all kinds of machine learning that must teach a learning algorithm without any known output or information. The most difficult thing in unsupervised learning is to evaluate whether the algorithm has learned something useful. Unsupervised learning…

Feature Engineering Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature engineering is fundamental to the application of machine learning, and is both difficult and expensive. The need for manual feature engineering can be obviated by automated feature learning(from : https://en.wikipedia.org/wiki/Feature_engineering )…

https://github.com/SeongjooHong/jupyter-notebook/blob/master/2020.01.11(pm)%20pandas%20tutorial.ipynb

1. pandas features pandas has the following characteristics: Easy handling of missing values Create data with automatic and explicit label position Data-intensive Advanced label-based slicing, extraction, and subset of large datasets Intuitive Dataset Combination Flexible transformation and transformation of datasets Descriptive labeling of axes Powerful I / O corresponding to various data formats Inherent Processing…

https://github.com/SeongjooHong/jupyter-notebook/blob/master/cost%20function%20and%20graduent%20descent%20and%20python%20class%20study.ipynb

https://github.com/SeongjooHong/jupyter-notebook/blob/master/diabetes%20regression%20study.ipynb