## Titanic: survival prediction

In this challenge, we need to analyse what sorts of people were likely to survive. In particular, we also need to apply the tools of machine learning to predict which passengers survived the tragedy.

## Resume of Decision Trees with Scikit-Learn

This blog introduces how CART algorithm works for classification and regression Decision Tree, how we understand and predict with the Decision Tree.

## Resume of Support Vector Machines

A Support Vector Machine (SVM) is a very powerful and versatile Machine Learning model, capable of performing linear or nonlinear classification and regression. It is one of the most popular models in Machine Learning. SVMs are suited for classification of complex but small- or medium-sized datasets.

## Resume of Logistic & Softmax Regression

In this blog, I will resume Logistic Regression, Softmax Regression and their usecases via Python.

## Transfering files with Putty

This blog introduces how to use `Putty` to transfer files from Windows OS to Linux OS and reverse.

## Resume of Regularized Linear Models

A good way to reduce overfitting is to regularize the model, which means the fewer degrees of freedom it has, the harder it will be for it to overfit the data. For a linear model, regularization is achieved by constraining the weights of the model. In this blog, I will talk about how to constrain the weights of Ridge Regression, Lasso Regression...

## Resume of Gradient Descent algorithm

In this blog, I resumed characteristics of 3 different Gradient Descent algorithms: Batch Gradient Descent computes the gradients based on the full training set, it takes long time; Stochastic Gradient Descent picks just one instance of training set, it has a better chance of finding the global minimum than Batch GD; Mini-batch Gradient Descent ...

## What is box plot ?

This blog talks about what is box plot, understanding box plot with help of probability density function (pdf), how to make a box plot by python module matplotlib and how to interprete a boxplot.

105 post articles, 14 pages.