Predictive Analytics Schulungen

Predictive Analytics Schulungen

Predictive Analytics courses

Testi...Client Testimonials

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
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
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
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
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
Piwik Getting started with Piwik 21 hours Audience Web analysist Data analysists Market researchers Marketing and sales professionals System administrators Format of course     Part lecture, part discussion, heavy hands-on practice 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  
datamodeling Pattern Recognition 35 hours This course provides an introduction into the field of pattern recognition and machine learning. It 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, instructor 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
matlabdsandreporting MATLAB Fundamentals, Data Science & Report Generation 126 hours In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform.  Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles. In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic. In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation. Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation. Assessments will be conducted throughout the course to gauge progress. Format of the course Course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation. Note Practice sessions will be based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange. Introduction MATLAB for data science and reporting   Part 01: MATLAB fundamentals Overview     MATLAB for data analysis, visualization, modeling, and programming. Working with the MATLAB user interface Overview of MATLAB syntax Entering commands     Using the command line interface Creating variables     Numeric vs character data Analyzing vectors and matrices     Creating and manipulating     Performing calculations Visualizing vector and matrix data Working with data files     Importing data from Excel spreadsheets Working with data types     Working with table data Automating commands with scripts     Creating and running scripts     Organizing and publishing your scripts Writing programs with branching and loops     User interaction and flow control Writing functions     Creating and calling functions     Debugging with MATLAB Editor Applying object-oriented programming principles to your programs   Part 02: MATLAB for data science Overview     MATLAB for data mining, machine learning and predictive analytics Accessing data     Obtaining data from files, spreadsheets, and databases     Obtaining data from test equipment and hardware     Obtaining data from software and the Web Exploring data     Identifying trends, testing hypotheses, and estimating uncertainty Creating customized algorithms Creating visualizations Creating models Publishing customized reports Sharing analysis tools     As MATLAB code     As standalone desktop or Web applications Using the Statistics and Machine Learning Toolbox Using the Neural Network Toolbox   Part 03: Report generation Overview     Presenting results from MATLAB programs, applications, and sample data     Generating Microsoft Word, PowerPoint®, PDF, and HTML reports.     Templated reports     Tailor-made reports         Using organization’s templates and standards Creating reports interactively vs programmatically     Using the Report Explorer     Using the DOM (Document Object Model) API Creating reports interactively using Report Explorer     Report Explorer Examples         Magic Squares Report Explorer Example     Creating reports         Using Report Explorer to create report setup file, define report structure and content     Formatting reports         Specifying default report style and format for Report Explorer reports     Generating reports         Configuring Report Explorer for processing and running report     Managing report conversion templates         Copying and managing Microsoft Word , PDF, and HTML conversion templates for Report Explorer reports     Customizing Report Conversion templates         Customizing the style and format of Microsoft Word and HTML conversion templates for Report Explorer reports     Customizing components and style sheets         Customizing report components, define layout style sheets Creating reports programmatically in MATLAB     Template-Based Report Object (DOM) API Examples         Functional report         Object-oriented report         Programmatic report formatting     Creating report content         Using the Document Object Model (DOM) API     Report format basics         Specifying format for report content     Creating form-based reports         Using the DOM API to fill in the blanks in a report form     Creating object-oriented reports         Deriving classes to simplify report creation and maintenance     Creating and formatting report objects         Lists, tables, and images     Creating DOM Reports from HTML         Appending HTML string or file to a Microsoft® Word, PDF, or HTML report generated by Document Object Model (DOM) API     Creating report templates         Creating templates to use with programmatic reports     Formatting page layouts         Formatting pages in Microsoft Word and PDF reports Summary and closing remarks
matlabpredanalytics Matlab for Predictive Analytics 21 hours Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events. In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data. By the end of this training, participants will be able to: Create predictive models to analyze patterns in historical and transactional data Use predictive modeling to identify risks and opportunities Build mathematical models that capture important trends Use data to from devices and business systems to reduce waste, save time, or cut costs Audience Developers Engineers Domain experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Introduction     Predictive analytics in finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing Overview of Big Data concepts Capturing data from disparate sources What are data-driven predictive models? Overview of statistical and machine learning techniques Case study: predictive maintenance and resource planning Applying algorithms to large data sets with Hadoop and Spark Predictive Analytics Workflow Accessing and exploring data Preprocessing the data Developing a predictive model Training, testing and validating a data set Applying different machine learning approaches ( time-series regression, linear regression, etc.) Integrating the model into existing web applications, mobile devices, embedded systems, etc. Matlab and Simulink integration with embedded systems and enterprise IT workflows Creating portable C and C++ code from MATLAB code Deploying predictive applications to large-scale production systems, clusters, and clouds Acting on the results of your analysis Next steps: Automatically responding to findings using Prescriptive Analytics Closing remarks

Kommende Kurse

CourseSchulungsdatumKurspreis (Fernkurs / Schulungsraum)
From Data to Decision with Big Data and Predictive Analytics - HannoverDi, 2017-12-26 09:302780EUR / 3430EUR
Applied Machine Learning - KölnMi, 2017-12-27 09:301890EUR / 2390EUR

Other regions

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Course Ort Schulungsdatum Kurspreis (Fernkurs / Schulungsraum)
Android - Grundkenntnisse Frankfurt am Main Di, 2017-12-26 09:30 3312EUR / 4112EUR
Fortgeschrittene "R"-Programmierung Hamburg Di, 2018-01-30 09:30 891EUR / 1241EUR
Introduction to Machine Learning Nürnberg Mi, 2018-04-04 09:30 891EUR / 1241EUR
Drupal and Solr Stuttgart Do, 2018-05-17 09:30 2457EUR / 2957EUR
Linux LPI LPIC-1 Exam 101 Vorbereitung Köln Di, 2018-07-03 09:30 1872EUR / 2372EUR

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