What Big Data, Data Science, Deep Learning software goes together? We analyze the associations between top Data Science tools, Commercial vs Free/Open Source, rank tools on R vs Python bias, find tools more associated with Big Data, those more associated with Deep Learning, and uncover strong regional differences. Last week, I reported the results of 2016 KDnuggets Software Poll: R, Python Duel As Top Analytics, Data Science software. This post looks a little deeper and examines the associations between different tools, their relationship to Big Data and Deep Learning, and regional patterns. At the end of the post there is a link to anonymized dataset, so that you can do your own analysis (and let me know about the results in comments below). The question asked in KDnuggets Poll was What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? First, we looked at associations between the top 10 tools. Lift (X & Y) = pct (X & Y) / ( pct (X) * pct (Y) ) where pct(X) is the percent of users who selected X.
Cheat Sheet - 10 Machine Learning Algorithms & R Commands - Bytes Cravings This article lists down 10 popular machine learning algorithms and related R commands (& package information) that could be used to create respective models. The objective is to represent a quick reference page for beginners/intermediate level R programmers who working on machine learning related problems. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are the different ML algorithms included in this article:Linear regressionLogistic RegressionK-Means ClusteringK-Nearest Neighbors (KNN) ClassificationNaive Bayes ClassificationDecison TreesSupport Vector Machine (SVM)Artifical Neural Network (ANN)AprioriAdaBoost Cheat Sheet – ML Algorithms & R Commands Linear regression: “lm” method from base package could be used for linear regression models. For most of the above formulas including linear regression model, one could use following function to predict: Ajitesh Kumar
12 Statistical and Machine Learning Methods that Every Data Scientist Should Know Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016. Statistical Hypothesis Testing (t-test, chi-squared test & ANOVA)Multiple Regression (Linear Models)General Linear Models (GLM: Logistic Regression, Poisson Regression)Random ForestXgboost (eXtreme Gradient Boosted Trees)Deep LearningBayesian Modeling with MCMCword2vecK-means ClusteringGraph Theory & Network Analysis(A1) Latent Dirichlet Allocation & Topic Modeling(A2) Factorization (SVD, NMF) From my experience in the data science industry for 4 years, I think that currently these 12 methods are the most popular, useful and suitable for various problems requiring data science. As far as I've known, there have been not a few lists of "representative methods in data science" ever. In addition to the list itself, I showed R or Python scripts of an experiment on sample datasets for each method, in order to enable readers to try it easily.
14 Great Machine Learning, Data Science, R , DataViz Cheat Sheets Data Science Central 14 Great Machine Learning, Data Science, R , DataViz Cheat Sheets by Laetitia Van Cauwenberge Oct 11, 2015 Your best references to do your job or get started in data science. Click here for picture source DSC Resources Additional Reading Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge Vincent Granville My data science cheat sheet is, I believe, the first one to have been published. 5 members like this loading
k-nearest neighbors algorithm - Wikipedia Non-parametric classification method In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Hodges in 1951,[1] and later expanded by Thomas Cover.[2] Most often, it is used for classification, as a k-NN classifier, the output of which is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. The k-NN algorithm can also be generalized for regression. For both classification and regression, a useful technique can be to assign weights to the contributions of the neighbors, so that nearer neighbors contribute more to the average than distant ones. The input consists of the k closest training examples in a data set. Statistical setting [edit] for ). on , let
Le « deep learning », une révolution dans l'intelligence artificielle Cette technologie d'apprentissage, basée sur des réseaux de neurones artificiels, a complètement bouleversé le domaine de l'intelligence artificielle en moins de cinq ans. Le Monde.fr | • Mis à jour le | Par Morgane Tual « Je n'ai jamais vu une révolution aussi rapide. On est passé d'un système un peu obscur à un système utilisé par des millions de personnes en seulement deux ans. » Yann LeCun, un des pionniers du « deep learning », n'en revient toujours pas. Après une longue traversée du désert, « l'apprentissage profond », qu'il a contribué à inventer, est désormais la méthode phare de l'intelligence artificielle (IA). Ce système d'apprentissage et de classification, basé sur des « réseaux de neurones artificiels » numériques, est, pêle-mêle, utilisé par Siri, Cortana et Google Now pour comprendre la voix, être capable d'apprendre à reconnaître des visages. Qu'est-ce que c'est ? « Comment reconnaître une image de chat ? Concrètement, ça donne quoi ? Et demain ?
IEEE Conference on Data Mining [April 22, 2009:] A companion book on The Top Ten Algorithms in Data Mining published in April 2009 [December 24, 2007:] A companion article in PDF for this top-10 algorithm initiative:Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. As the first step in the identification process, in September 2006 we invited the ACM KDD Innovation Award and IEEE ICDM Research Contributions Award winners to each nominate up to 10 best-known algorithms in data mining. After the nominations in Step 1, we verified each nomination for its citations on Google Scholar in late October 2006, and removed those nominations that did not have at least 50 citations. 18 Candidates for the Top 10 Algorithms in Data Mining In the third step of the identification process, we had a wider involvement of the research community.
josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software. Start Here With Machine Learning Get Started and Get Good at Applied Machine Learning Hi, Jason here. I’m the guy behind Machine Learning Mastery. My goal is to help you get started, make progress and kick butt with machine learning. I teach a top-down and results-first approach designed for developers and engineers. Access my best free tutorials on the blog or take the next step with my paid training material. You may be feeling overwhelmed. Take your time. Table of Contents What do you need help with? How Do I Get Started? The most common question I’m asked is: “how do I get started?” My best advice for getting started in machine learning is broken down into a 5-step process: For more on this top-down approach, see: Many of my students have used this approach to go on and do well in Kaggle competitions and get jobs as Machine Learning Engineers and Data Scientists. Applied Machine Learning Process The benefit of machine learning are the predictions and the models that make predictions. Machine Learning Algorithms Deep Learning
Machine Learning Repository Tutorial To Implement k-Nearest Neighbors in Python From Scratch The k-Nearest Neighbors algorithm (or kNN for short) is an easy algorithm to understand and to implement, and a powerful tool to have at your disposal. In this tutorial you will implement the k-Nearest Neighbors algorithm from scratch in Python (2.7). The implementation will be specific for classification problems and will be demonstrated using the Iris flowers classification problem. This tutorial is for you if you are a Python programmer, or a programmer who can pick-up python quickly, and you are interested in how to implement the k-Nearest Neighbors algorithm from scratch. k-Nearest Neighbors algorithm Image from Wikipedia, all rights reserved What is k-Nearest Neighbors The model for kNN is the entire training dataset. The similarity measure is dependent on the type of data. In the case of regression problems, the average of the predicted attribute may be returned. How does k-Nearest Neighbors Work Get your FREE Algorithms Mind Map Download it, print it and use it. Download For Free 1. 2.