Predictive Analytics Schulungen

Predictive Analytics courses

Predictive Analytics Schulungsübersicht

ID Name Dauer Übersicht
148163 Angewandtes Maschinelles Lernen 14 hours Der Übungskurs ist für alle diejenigen gedacht, die "Machine Learning" in praktischen Applikationen anwenden möchten Teilnehmer Dieser Kurs ist für Data Scientists und Statistiker, die Grundkenntnisse in Statistik haben und wissen, wie man R programmiert. Der Schwerpunkt des Kurses liegt auf dem praktischen Aspekt von Daten/Modell-Vorbereitung, Execution, post hoc Analyse und Visualisierung. Das Ziel ist es, den Teilnehmern praktische Kenntnisse im Maschinellen Lernen  zu vermitteln.  Bereichsspezifische Beispiele erhöhen die Relevanz der Schulung für die Teilnehmer.  Naive Bayes Multinomial Modelle Bayesian categorical Datenanalyse Diskriminante Analyse Lineare Regression Logistischge Regression GLM EM Algorithm Mixed Models Zusätzliche Modelle Klassifikation KNN Bayesian Graphik-Modelle Factor Analysis (FA) Principal Component Analysis (PCA) Independent Component Analysis (ICA) Support Vector Machines (SVM) für Regression und Klassifikation Boosting Ensemble Modelle Neural networks Hidden Markov Models (HMM) Space State Modelle Clustering
148167 Apache Mahout für Entwickler 14 hours Teilnehmer   Entwickler, die in ihren Projekten Apache Mahout für maschinelles Lernen nutzen möchten.     Inhalt Praktische EInführung in maschinelles Lernen. Der Kurs wird in Form eines Workshops durchgeführt und beinhaltet Anwendungsfälle zu realen Problemen.    Implementierung von Empfehlungssystemen mittels Mahout Einführung in Empfehlungsdienste Darstellung Empfehlungsdienste Empfehlungen erstellen Empfehlungen optimieren Clustering Grundlagen des Clustering Datenrepräsentation Clustering Algorithmen Clustering Qualitätsverbesserungen Optimieren der Clustering-Implementierung Anwendung von Clustering in der Praxis Klassifikation Grundlagen der Klassifikation Klassifizierungstraining  Qualitätsverbesserung des Klassifikators
148190 From Data to Decision with Big Data and Predictive Analytics 21 hours Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. Quick Overview Data Sources Minding Data Recommender systems Target Marketing Datatypes Structured vs unstructured Static vs streamed Attitudinal, behavioural and demographic data Data-driven vs user-driven analytics data validity Volume, velocity and variety of data Models Building models Statistical Models Machine learning Data Classification Clustering kGroups, k-means, nearest neighbours Ant colonies, birds flocking Predictive Models Decision trees Support vector machine Naive Bayes classification Neural networks Markov Model Regression Ensemble methods ROI Benefit/Cost ratio Cost of software Cost of development Potential benefits Building Models Data Preparation (MapReduce) Data cleansing Choosing methods Developing model Testing Model Model evaluation Model deployment and integration Overview of Open Source and commercial software Selection of R-project package Python libraries Hadoop and Mahout Selected Apache projects related to Big Data and Analytics Selected commercial solution Integration with existing software and data sources
234942 Programming with Big Data in R 21 hours Introduction to Programming Big Data with R (bpdR) Setting up your environment to use pbdR Scope and tools available in pbdR Packages commonly used with Big Data alongside pbdR Message Passing Interface (MPI) Using pbdR MPI 5 Parallel processing Point-to-point communication Send Matrices Summing Matrices Collective communication Summing Matrices with Reduce Scatter / Gather Other MPI communications Distributed Matrices Creating a distributed diagonal matrix SVD of a distributed matrix Building a distributed matrix in parallel Statistics Applications Monte Carlo Integration Reading Datasets Reading on all processes Broadcasting from one process Reading partitioned data Distributed Regression Distributed Bootstrap
566938 Predictive Modelling with R 14 hours Problems facing forecasters Customer demand planning Investor uncertainty Economic planning Seasonal changes in demand/utilization Roles of risk and uncertainty Time series Forecasting Seasonal adjustment Moving average Exponential smoothing Extrapolation Linear prediction Trend estimation Stationarity and ARIMA modelling Econometric methods (casual methods) Regression analysis Multiple linear regression Multiple non-linear regression Regression validation Forecasting from regression Judgemental methods Surveys Delphi method Scenario building Technology forecasting Forecast by analogy Simulation and other methods Simulation Prediction market Probabilistic forecasting and Ensemble forecasting


Course Ort Schulungsdatum Kurspreis (Fernkurs/Schulungsraum)
ORACLE SQL Grundlagen Graz Mo, 2016-06-06 09:30 2693EUR / 3343EUR

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