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 …
2020.03.14(pm): Hierarchical Clustering
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 …
2020.03.08(am): K-means Clustering
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 …
2020.01.18(pm): Feature Selection and Dimension reduction
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 ) …
Continue reading “2020.01.18(pm): Feature Selection and Dimension reduction”
2020.01.11(pm): pandas tutorial
https://github.com/SeongjooHong/jupyter-notebook/blob/master/2020.01.11(pm)%20pandas%20tutorial.ipynb
2020.01.01.(pm): Statistical analysis using pandas
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 …
Continue reading “2020.01.01.(pm): Statistical analysis using pandas”
2019.12.29(pm): Cost function and Python class
https://github.com/SeongjooHong/jupyter-notebook/blob/master/cost%20function%20and%20graduent%20descent%20and%20python%20class%20study.ipynb
2019.12.28(pm): Regression using scikit-learn diabetes data
https://github.com/SeongjooHong/jupyter-notebook/blob/master/diabetes%20regression%20study.ipynb
2019.12.16(pm): How to Change Jupyter notebook Initial Path Settings
When using the Jupyter notebook, the initial path is often C: \ Users \ USERNAME. In this case, It is difficult to manage python code, and C drives become a mess. So users run Jupyter notebook, go to Desktop, create a new folder and work on it. Because doing this repetitive task every time is …
Continue reading “2019.12.16(pm): How to Change Jupyter notebook Initial Path Settings”
2019.12.16(pm): How to Troubleshoot SSL (HTTP) Issues
If you get the following error code, there is a security problem. The causes can be reduced to three things: communication errors, firewall blocking, and SSL connection restriction. The command to solve is: You may want to reinstall SSL, reinstall previous version of Anaconda. If this doesn’t work, please let me know.