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Weka 3 - Data Mining with Open Source Machine Learning Software in Java

Weka 3 - Data Mining with Open Source Machine Learning Software in Java
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. Weka is open source software issued under the GNU General Public License. Yes, it is possible to apply Weka to big data! Data Mining with Weka is a 5 week MOOC, which was held first in late 2013.

COC131 Data Mining, Tuotorials Weka "The overall goal of our project is to build a state-of-the-art facility for developing machine learning (ML) techniques and to apply them to real-world data mining problems. Our team has incorporated several standard ML techniques into a software "workbench" called WEKA, for Waikato Environment for Knowledge Analysis. Tutorial 01 (13/02/09) Get the old faithful data-set (.csv) here Get the tutorial 01 exercises here Get the tutorial 01 solutions here Statistics revision for Tutorial 01 here Tutorial 02 (20/02/09) Get the iris data-set (.arff) here Get the tutorial 02 exercises here Tutorial 03 (27/02/09) Get the tutorial 03 exercises here Tutorial 04 (06/03/09) Tutorial 03 exercises and clarification of any issues from earlier tutorials Tutorial 05 (13/03/09) Get the tutorial 04 exercises here Tutorial 06 (20/03/09) Get the flags data-set (.arff) here Get the whole euro data-set (.arff) here Get the tutorial 05 exercises here Tutorial 07 (27/03/09) Tutorial 08 (24/04/09) Coursework

GGobi data visualization system. Implementation of k-means Clustering - Edureka In this blog, you will understand what is K-means clustering and how it can be implemented on the criminal data collected in various US states. The data contains crimes committed like: assault, murder, and rape in arrests per 100,000 residents in each of the 50 US states in 1973. Along with analyzing the data you will also learn about: Finding the optimal number of clusters.Minimizing distortionCreating and analyzing the elbow curve.Understanding the mechanism of k-means algorithm. Let us start with the analysis. The data looks as: Click on the image to download this dataset Need this dataset? First let’s prepare the data for the analysis. > crime0 <- na.omit(USArrests) > crime <- data.matrix (crime0) > str(crime) num [1:50, 1:4] 13.2 10 8.1 8.8 9 7.9 3.3 5.9 15.4 17.4 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:50] "Alabama" "Alaska" "Arizona" "Arkansas" ... ..$ : chr [1:4] "Murder" "Assault" "UrbanPop" "Rape" Let us take the number of clusters to be 5. Analyzing the Clustering :

Octave GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is distributed under the terms of the GNU General Public License. Version 4.0.0 has been released and is now available for download. An official Windows binary installer is also available from A list of important user-visible changes is availble at by selecting the Release Notes item in the News menu of the GUI, or by typing news at the Octave command prompt. Thanks to the many people who contributed to this release!

Home - SCaVis Freedom to choose a programming language. Freedom to choose an operating system. Freedom to share your code. Supported programming languages SCaVis can be used with several scripting languages for the Java platform, such as BeanShell, Jython (the Python programming language), Groovy and JRuby (Ruby programming language). Supported platforms SCaVis runs on Windows, Linux, Mac and Android operating systems. SCaVis is a successor of the popular jHepWork package which has been under intensive development since 2005.

A Guide to Deep Learning by YerevaNN When you are comfortable with the prerequisites, we suggest four options for studying deep learning. Choose any of them or any combination of them. The number of stars indicates the difficulty. Hugo Larochelle's video course on YouTube. There are many software frameworks that provide necessary functions, classes and modules for machine learning and for deep learning in particular. Jupyter notebooks are a convenient way to play with Python code.

Weka---Machine Learning Software in Java | Free software downloads The R Project for Statistical Computing What is machine learning? Everything you need to know This device is unable to play the requested video. Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people. From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence -- helping software make sense of the messy and unpredictable real world. But what exactly is machine learning and what is making the current boom in machine learning possible? What is machine learning? At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video. What is AlphaGo?

Apache Mahout: Scalable machine learning and data mining Une introduction aux arbres de décision Les arbres de décision sont l’une des structures de données majeures de l’apprentissage statistique. Leur fonctionnement repose sur des heuristiques qui, tout en satisfaisant l’intuition, donnent des résultats remarquables en pratique (notamment lorsqu’ils sont utilisés en « forêts aléatoires »). Leur structure arborescente les rend également lisibles par un être humain, contrairement à d’autres approches où le prédicteur construit est une « boîte noire ». L’introduction que nous proposons ici décrit les bases de leur fonctionnement tout en apportant quelques justifications théoriques. Nous aborderons aussi (brièvement) l’extension aux Random Forests. Suivez le lien pour la version PDF. Table des matières Un arbre de décision modélise une hiérarchie de tests sur les valeurs d’un ensemble de variables appelées attributs. 1 Construction d’un arbre de décision Condition d’arrêt : elle influe sur la profondeur et la précision du prédicteur produit. Meilleur attribut : 2 Régression Taux d’erreur :