Predictive Analytics Schulungen

Predictive Analytics Schulungen

Predictive Analytics courses


Predictive Modelling with R

He was very informative and helpful.

Pratheep Ravy - UPC Schweiz GmbH

Applied Machine Learning

ref material to use later was very good

PAUL BEALES - Seagate Technology

Predictive Analytics Schulungsübersicht

Code Name Dauer Übersicht
apachemdev 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
d2dbdpa 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
appliedml 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
predmodr 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
intror Introduction to R with Time Series Analysis 21 hours Introduction and preliminaries Making R more friendly, R and available GUIs Rstudio Related software and documentation R and statistics Using R interactively An introductory session Getting help with functions and features R commands, case sensitivity, etc. Recall and correction of previous commands Executing commands from or diverting output to a file Data permanency and removing objects Simple manipulations; numbers and vectors Vectors and assignment Vector arithmetic Generating regular sequences Logical vectors Missing values Character vectors Index vectors; selecting and modifying subsets of a data set Other types of objects Objects, their modes and attributes Intrinsic attributes: mode and length Changing the length of an object Getting and setting attributes The class of an object Arrays and matrices Arrays Array indexing. Subsections of an array Index matrices The array() function The outer product of two arrays Generalized transpose of an array Matrix facilities Matrix multiplication Linear equations and inversion Eigenvalues and eigenvectors Singular value decomposition and determinants Least squares fitting and the QR decomposition Forming partitioned matrices, cbind() and rbind() The concatenation function, (), with arrays Frequency tables from factors Lists and data frames Lists Constructing and modifying lists Concatenating lists Data frames Making data frames attach() and detach() Working with data frames Attaching arbitrary lists Managing the search path Data manipulation Selecting, subsetting observations and variables           Filtering, grouping Recoding, transformations Aggregation, combining data sets Character manipulation, stringr package Reading data Txt files CSV files XLS, XLSX files SPSS, SAS, Stata,… and other formats data Exporting data to txt, csv and other formats Accessing data from databases using SQL language Probability distributions R as a set of statistical tables Examining the distribution of a set of data One- and two-sample tests Grouping, loops and conditional execution Grouped expressions Control statements Conditional execution: if statements Repetitive execution: for loops, repeat and while Writing your own functions Simple examples Defining new binary operators Named arguments and defaults The '...' argument Assignments within functions More advanced examples Efficiency factors in block designs Dropping all names in a printed array Recursive numerical integration Scope Customizing the environment Classes, generic functions and object orientation Graphical procedures High-level plotting commands The plot() function Displaying multivariate data Display graphics Arguments to high-level plotting functions Basic visualisation graphs Multivariate relations with lattice and ggplot package Using graphics parameters Graphics parameters list 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
datamodeling Pattern Recognition 35 hours This course provides an introduction into the field of pattern recognition and machine learning. It also touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, continuous feedback, and testing of knowledge and skills acquired. Audience     Data analysts     PhD students, researchers and practitioners   Introduction Probability theory, model selection, decision and information theory Probability distributions Linear models for regression and classification Neural networks Kernel methods Sparse kernel machines Graphical models Mixture models and EM Approximate inference Sampling methods Continuous latent variables Sequential data Combining models  
kdd Knowledge Discover in Databases (KDD) 21 hours Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing. In this course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes. Audience     Data analysts or anyone interested in learning how to interpret data to solve problems Format of the course     After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations. Introduction     KDD vs data mining Establishing the application domain Establishing relevant prior knowledge Understanding the goal of the investigation Creating a target data set Data cleaning and preprocessing Data reduction and projection Choosing the data mining task Choosing the data mining algorithms Interpreting the mined patterns
Piwik Getting started with Piwik 21 hours Web analysist Data analysists Market researchers Marketing and sales professionals System administrators Format of course 30% lectures 60% exercises 10% tests Introduction to Piwik Why use Piwik? Piwik vs Google Analystics Setting up Piwik Selecting which websites to monitor Working with the dashboard Understanding visitor activity Actions Referrals Generating reports  
bigdatar 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

Kommende Kurse

CourseSchulungsdatumKurspreis (Fernkurs / Schulungsraum)
Apache Mahout for Developers - ZürichMi, 2017-06-07 09:301800EUR / 2300EUR
From Data to Decision with Big Data and Predictive Analytics - BernMi, 2017-06-07 09:302780EUR / 3430EUR
Applied Machine Learning - BaselMi, 2017-06-07 09:301890EUR / 2390EUR

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