# Machine-Learning

Reviews and projects of Machine Learning models / algorithms, such as:

• Regression
• Decision Trees
• Support Vector Machines
• Gradient Descent algorithm,
• etc.

## House Prices: Regression Techniques

In this challenge, we need to predict the sales price for each house.

## Resume of Ensemble Learning

In this blog, I introduced 3 Ensemble Learning algorithms: Voting Classifiers, Bagging and Pasting, Random Forests.

## 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...

## Resume of Logistic & Softmax Regression

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

## 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...

## 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 Grad...

## Association analysis - Apriori algorithm

Have your heard about the classic use case of association analysis - “Beer and diaper” at Walmart? In this story, Walmart found that beer and diapers were often sold together, we can use associatio...

## R IN ACTION Review 5 - Time series (Part 3)

In this blog, I’ll introduce ARIMA forecasting models. In the autoregressive integrated moving average (ARIMA) approach to forecasting, predicted values are a linear function of recent actual value...

## R IN ACTION Review 4 - Time series (Part 2)

In this blog, we’ll turn to forecasting, starting with popular exponential modeling approaches that use weighted averages of time-series values[1]. Exponential models are some of the most popular ...

## R IN ACTION Review 3 - Time series (Part 1)

I learnt Time Series from “R IN ACTION” in recent days and want to extract some important points for absorbing and summarizing the knowledge[1]. In this blog, I will simply introduce the methods fo...

## Decision Tree

Applying decision tree model in R's library 'rpart', using rpart function to build a model, showing fancyRpartPlot example, predicting with predict(), pruning with prune().

## Training set and Test set

As I mentioned in my blog “What is Machine Learning”, Machine Learning tasks are typically classified into three broad categories, one of them is Supervised learning. In the supervised learning set...

## What is Machine Learning?

Machine learning explores the study and construction of algorithms that can learn from and perform predictive analysis on data. Such algorithms operate by building a model from an example training ...

## Measuring model performance or error

How to find out whether the Machine Learning I did is good or not? (i.e. the quality of our model) Well, it depends. First of all, we have to find performance, which depends on the context in which...