The two main types of machine learning algorithms are supervised and unsupervised learning. There are many types of supervised algorithms available, one of the most popular ones is the Naive Bayes model which is often a good starting point for developers since it's fairly easy to understand the underlying probabilistic model and easy to execute. Decision trees are also a predictive model and have two types of trees: regression (which take continuous values) and classification models (which take finite values) and use a divide and conquer strategy that recursively separates the data to generate the tree. Check out the rest of the blog for more resources on natural language processing and machine learning algorithms such as LDA for text classification or increasing the accuracy on a Nudity Detection algorithm and a beginners tutorial on using Scikit-learn to solve FizzBuzz.

In last week's post I explored whether machine learning models can be applied to predict flu deaths from the 2013 outbreak of influenza A H7N9 in China. There, I compared random forests, elastic-net regularized generalized linear models, k-nearest neighbors, penalized discriminant analysis, stabilized linear discriminant analysis, nearest shrunken centroids, single C5.0 tree and partial least squares. Extreme gradient boosting (XGBoost) is a faster and improved implementation of gradient boosting for supervised learning and has recently been very successfully applied in Kaggle competitions. Because I've heard XGBoost's praise being sung everywhere lately, I wanted to get my feet wet with it too. So this week I want to compare the prediction success of gradient boosting with the same dataset.

Regression is a time-tested manner for approximating relationships among a given collection of data, and the recipient of unhelpful naming via unfortunate circumstances. Linear regression is a simple algebraic tool which attempts to find the "best" (generally straight) line fitting 2 or more attributes, with one attribute (simple linear regression), or a combination of several (multiple linear regression), being used to predict another, the class attribute. A set of training instances is used to compute the linear model, with one attribute, or a set of attributes, being plotted against another. The model then attempts to identify where new instances would lie on the regression line, given a particular class attribute. It is often confusing for people without a sufficient math background to understand how matrix multiplication fits into linear regression.

Founded in 2010, Kaggle is a Data Science platform where users can share, collaborate, and compete. One key feature of Kaggle is "Competitions", which offers users the ability to practice on real world data and to test their skills with, and against, an international community. This guide will teach you how to approach and enter a Kaggle competition, including exploring the data, creating and engineering features, building models, and submitting predictions. We'll follow these steps to a successful Kaggle Competition submission: We need to acquire the data for the competition. The descriptions of the features and some other helpful information are contained in a file with an obvious name, data_description.txt. Download the data and save it into a folder where you'll keep everything you need for the competition. We will first look at the train.csv After we've trained a model, we'll make predictions using the test.csv

In the recent years, reciprocal link prediction has received some attention from the data mining and social network analysis researchers, who solved this problem as a binary classification task. However, it is also important to predict the interval time for the creation of reciprocal link. This is a challenging problem for two reasons: First, the lack of effective features, because well-known link prediction features are designed for undirected networks and for the binary classification task, hence they do not work well for the interval time prediction; Second, the presence of censored data instances makes the traditional supervised regression methods unsuitable for solving this problem. In this paper, we propose a solution for the reciprocal link interval time prediction task. We map this problem into survival analysis framework and show through extensive experiments on real-world datasets that, survival analysis methods perform better than traditional regression, neural network based model and support vector regression (SVR).

A number of open-source communities are using python to make available artificial intelligence and machine learning related packages and libraries. In this blog I will use libraries from scikit-learn. Project scikit-learn is a Machine Learning Project in Python. It has a good collection of algorithms for some of the well known data-mining and data analysis jobs such as for Classification, Regression, Clustering, Dimensionality reduction and Model Selection. These algorithms are constructed on a stack of NumPy, SciPy library, and matplotlib.

The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems. A support vector machine (SVM) training algorithm finds the classifier represented by the normal vector \(w\) and bias \(b\) of the hyperplane. A support vector machine (SVM) training algorithm finds the classifier represented by the normal vector and bias of the hyperplane. Support vector machines (SVM) and other simpler models, which can be easily trained by solving convex optimization problems, gradually replaced neural networks in machine learning.

It's a common task for a data scientist: you need to generate segments (or clusters- I'll use the terms interchangably) of the customer base. With definitions, of course!!! Clustering is the subfield of unsupervised learning that aims to partition unlabelled datasets into consistent groups based on some shared unknown characteristics. All the tools you'll need are in Scikit-Learn, so I'll leave the code to a minimum. Instead, through the medium of GIFs, this tutorial will describe the most common techniques. If GIFs aren't your thing (what are you doing on the internet?), You can download this jupyter notebook here and the gifs can be downloaded from this folder (or you can just right click on the GIFs and select'Save image as…'). Clustering algorithms can be broadly split into two types, depending on whether the number of segments is explicitly specified by the user.

In this post, we consider different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered. Time series analysis, especially forecasting, is an important problem of modern predictive analytics.

In this blog post, I'll help you get started using Apache Spark's spark.ml Classification is a family of supervised machine learning algorithms that identify which category an item belongs to (for example, whether a cancer tissue observation is malignant or not), based on labeled examples of known items (for example, observations known to be malignant or not). Classification takes a set of data with known labels and pre-determined features and learns how to label new records based on that information. Features are the "if questions" that you ask. The label is the answer to those questions.