Neural Networks Training Courses

Neural Networks Training

Neural Networks courses

Client Testimonials

Artificial Neural Networks, Machine Learning, Deep Thinking

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease - Knowledgepool Group Ltd

Introduction to the use of neural networks

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq - Tricentis GmbH

Introduction to the use of neural networks

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq - Tricentis GmbH

Introduction to the use of neural networks

the interactive part, tailored to our specific needs

Thomas Stocker - Tricentis GmbH

From Data to Decision with Big Data and Predictive Analytics

zakres materialu

Maciej Jonczyk - Orange Polska

From Data to Decision with Big Data and Predictive Analytics

usystematyzowanie wiedzy z dziedziny ML

- Orange Polska

Applied Machine Learning

ref material to use later was very good

PAUL BEALES - Seagate Technology

Introduction to Deep Learning

The topic is very interesting

Wojciech Baranowski - Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Trainers theoretical knowledge and willingness to solve the problems with the participants after the training

Grzegorz Mianowski - Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Topic. Very interesting!

Piotr - Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.

- Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Interesting subject

Wojciech Wilk - Dolby Poland Sp. z o.o.

Neural Networks Course Outlines

Code Name Duration Overview
deeplearning1 Introduction to Deep Learning 21 hours This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction. Backprop, modular models Logsum module RBF Net MAP/MLE loss Parameter Space Transforms Convolutional Module Gradient-Based Learning  Energy for inference, Objective for learning PCA; NLL:  Latent Variable Models Probabilistic LVM Loss Function Handwriting recognition
sysagent Systemy wieloagentowe 7 hours
pjn Przetwarzanie języka naturalnego 7 hours
iop Inteligencja obliczeniowa w praktyce 7 hours
mtdintob Metody Inteligencji Obliczeniowej 7 hours
cntk Using Computer Network ToolKit (CNTK) 28 hours Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images. Audience This course is directed at engineers and architects aiming to utilize CNTK in their projects. Getting started Setup CNTK on your machine Enabling 1bit SGD Developing and Testing CNTK Production Test Configurations How to contribute to CNTK Tutorial Tutorial II CNTK usage overview Examples Presentations Multiple GPUs¹ and machines Configuring CNTK Config file overview Simple Network Builder BrainScript Network Builder SGD block Reader block Train, Test, Eval Top-level configurations Describing Networks Basic concepts Expressions Defining functions Full Function Reference Data readers Text Format Reader CNTK Text Format Reader UCI Fast Reader (deprecated) HTKMLF Reader LM sequence reader LU sequence reader Image reader Evaluating CNTK Models Overview C++ Evaluation Interface C# Evaluation Interface Evaluating Hidden Layers C# Image Transforms for Evaluation Advanced topics Command line parsing rules Top-level commands Plot command ConvertDBN command ¹ The topic related to the use of CNTK with a GPU is not available as a part of a remote course. This module can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.
aiintrozero From Zero to AI 35 hours This course is created for people who have no previous experience in probability and statistics. Probability (3.5h) Definition of probability Binomial distribution Everyday usage exercises Statistics (10.5h) Descriptive Statistics Inferential Statistics Regression Logistic Regression Exercises Intro to programming (3.5h) Procedural Programming Functional Programming OOP Programming Exercises (writing logic for a game of choice, e.g. noughts and crosses) Machine Learning (10.5h) Classification Clustering Neural Networks Exercises (write AI for a computer game of choice) Rules Engines and Expert Systems (7 hours) Intro to Rule Engines Write AI for the same game and combing solutions into hybrid approach
aiauto Artificial Intelligence in Automotive 14 hours This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making. Current state of the technology What is used What may be potentially used Rules based AI  Simplifying decision Machine Learning  Classification Clustering Neural Networks Types of Neural Networks Presentation of working examples and discussion Deep Learning Basic vocabulary  When to use Deep Learning, when not to Estimating computational resources and cost Very short theoretical background to Deep Neural Networks Deep Learning in practice (mainly using TensorFlow) Preparing Data Choosing loss function Choosing appropriate type on neural network Accuracy vs speed and resources Training neural network Measuring efficiency and error Sample usage Anomaly detection Image recognition ADAS        
aiint Artificial Intelligence Overview 7 hours This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and everyone who is interested overview of applied artificial intelligence and the nearest forecast for its development. Artificial Intelligence History Intelligent Agents Problem Solving Solving Problems by Searching Beyond Classical Search Adversarial Search Constraint Satisfaction Problems Knowledge and Reasoning Logical Agents First-Order Logic Inference in First-Order Logic Classical Planning Planning and Acting in the Real World Knowledge Representation Uncertain Knowledge and Reasoning Quantifying Uncertainty Probabilistic Reasoning Probabilistic Reasoning over Time Making Simple Decisions Making Complex Decisions Learning Learning from Examples Knowledge in Learning Learning Probabilistic Models Reinforcement Learning Communicating, Perceiving, and Acting; Natural Language Processing Natural Language for Communication Perception Robotics Conclusions Philosophical Foundations AI: The Present and Future
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example 28 hours This course will give you knowledge in neural networks and generally in machine learning algorithm,  deep learning (algorithms and applications). This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. TensorFlow Basics Creation, Initializing, Saving, and Restoring TensorFlow variables Feeding, Reading and Preloading TensorFlow Data How to use TensorFlow infrastructure to train models at scale Visualizing and Evaluating models with TensorBoard TensorFlow Mechanics Inputs and Placeholders Build the GraphS Inference Loss Training Train the Model The Graph The Session Train Loop Evaluate the Model Build the Eval Graph Eval Output The Perceptron Activation functions The perceptron learning algorithm Binary classification with the perceptron Document classification with the perceptron Limitations of the perceptron From the Perceptron to Support Vector Machines Kernels and the kernel trick Maximum margin classification and support vectors Artificial Neural Networks Nonlinear decision boundaries Feedforward and feedback artificial neural networks Multilayer perceptrons Minimizing the cost function Forward propagation Back propagation Improving the way neural networks learn Convolutional Neural Networks Goals Model Architecture Principles Code Organization Launching and Training the Model Evaluating a Model
neuralnet Introduction to the use of neural networks 7 hours The training is aimed at people who want to learn the basics of neural networks and their applications. The Basics Whether computers can think of? Imperative and declarative approach to solving problems Purpose Bedan on artificial intelligence The definition of artificial intelligence. Turing test. Other determinants The development of the concept of intelligent systems Most important achievements and directions of development Neural Networks The Basics Concept of neurons and neural networks A simplified model of the brain Opportunities neuron XOR problem and the nature of the distribution of values The polymorphic nature of the sigmoidal Other functions activated Construction of neural networks Concept of neurons connect Neural network as nodes Building a network Neurons Layers Scales Input and output data Range 0 to 1 Normalization Learning Neural Networks Backward Propagation Steps propagation Network training algorithms range of application Estimation Problems with the possibility of approximation by Examples XOR problem Lotto? Equities OCR and image pattern recognition Other applications Implementing a neural network modeling job predicting stock prices of listed Problems for today Combinatorial explosion and gaming issues Turing test again Over-confidence in the capabilities of computers
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  
rneuralnet Neural Network in R 14 hours This course is an introduction to applying neural networks in real world problems using R-project software. Introduction to Neural Networks What are Neural Networks What is current status in applying neural networks Neural Networks vs regression models Supervised and Unsupervised learning Overview of packages available nnet, neuralnet and others differences between packages and itls limitations Visualizing neural networks Applying Neural Networks Concept of neurons and neural networks A simplified model of the brain Opportunities neuron XOR problem and the nature of the distribution of values The polymorphic nature of the sigmoidal Other functions activated Construction of neural networks Concept of neurons connect Neural network as nodes Building a network Neurons Layers Scales Input and output data Range 0 to 1 Normalization Learning Neural Networks Backward Propagation Steps propagation Network training algorithms range of application Estimation Problems with the possibility of approximation by Examples OCR and image pattern recognition Other applications Implementing a neural network modeling job predicting stock prices of listed
Torch Torch: Getting started with Machine and Deep Learning 21 hours Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others. In this course we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned. By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects. Audience     Software developers and programmers wishing to enable Machine and Deep Learning within their applications Format of the course     Overview of Machine and Deep Learning     In-class coding and integration exercises     Test questions sprinkled along the way to check understanding Introduction to Torch     Like NumPy but with CPU and GPU implementation     Torch's usage in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking Installing Torch     Linux, Windows, Mac     Bitmapi and Docker Installing Torch packages     Using the LuaRocks package manager Choosing an IDE for Torch     ZeroBrane Studio     Eclipse plugin for Lua Working with the Lua scripting language and LuaJIT     Lua's integration with C/C++     Lua syntax: datatypes, loops and conditionals, functions, functions, tables, and file i/o.     Object orientation and serialization in Torch     Coding exercise Loading a dataset in Torch     MNIST     CIFAR-10, CIFAR-100     Imagenet Machine Learning in Torch     Deep Learning         Manual feature extraction vs convolutional networks     Supervised and Unsupervised Learning         Building a neural network with Torch         N-dimensional arrays Image analysis with Torch     Image package     The Tensor library Working with the REPL interpreter Working with databases Networking and Torch GPU support in Torch Integrating Torch     C, Python, and others Embedding Torch     iOS and Android Other frameworks and libraries     Facebook's optimized deep-learning modules and containers Creating your own package Testing and debugging Releasing your application The future of AI and Torch
MLFWR1 Machine Learning Fundamentals with R 14 hours The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. Introduction to Applied Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Regression Linear regression Generalizations and Nonlinearity Exercises Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means
OpenNN OpenNN: Implementing neural networks 14 hours OpenNN is an open-source class library written in C++  which implements neural networks, for use in machine learning. In this course we go over the principles of neural networks and use OpenNN to implement a sample application. Audience     Software developers and programmers wishing to create Deep Learning applications. Format of the course     Lecture and discussion coupled with hands-on exercises. Introduction to OpenNN, Machine Learning and Deep Learning Downloading OpenNN Working with Neural Designer     Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics OpenNN architecture     CPU parallelization OpenNN classes     Data set, neural network, loss index, training strategy, model selection, testing analysis     Vector and matrix templates Building a neural network application     Choosing a suitable neural network     Formulating the variational problem (loss index)     Solving the reduced function optimization problem (training strategy) Working with datasets      The data matrix (columns as variables and rows as instances) Learning tasks     Function regression     Pattern recognition Compiling with QT Creator Integrating, testing and debugging your application The future of neural networks and OpenNN
appliedml Applied Machine Learning 14 hours This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience. Naive Bayes Multinomial models Bayesian categorical data analysis Discriminant analysis Linear regression Logistic regression GLM EM Algorithm Mixed Models Additive Models Classification KNN Bayesian Graphical Models Factor Analysis (FA) Principal Component Analysis (PCA) Independent Component Analysis (ICA) Support Vector Machines (SVM) for regression and classification Boosting Ensemble models Neural networks Hidden Markov Models (HMM) Space State Models Clustering
mldt Machine Learning and Deep Learning 21 hours This course covers AI (emphasizing Machine Learning and Deep Learning)Machine learning Introduction to Machine Learning Applications of machine learning Supervised Versus Unsupervised Learning Machine Learning Algorithms Regression Classification Clustering Recommender System Anomaly Detection Reinforcement Learning Regression Simple & Multiple Regression Least Square Method Estimating the Coefficients Assessing the Accuracy of the Coefficient Estimates Assessing the Accuracy of the Model Post Estimation Analysis Other Considerations in the Regression Models Qualitative Predictors Extensions of the Linear Models Potential Problems Bias-variance trade off [under-fitting/over-fitting] for regression models Resampling Methods Cross-Validation The Validation Set Approach Leave-One-Out Cross-Validation k-Fold Cross-Validation Bias-Variance Trade-Off for k-Fold The Bootstrap Model Selection and Regularization Subset Selection [Best Subset Selection, Stepwise Selection, Choosing the Optimal Model] Shrinkage Methods/ Regularization [Ridge Regression, Lasso & Elastic Net] Selecting the Tuning Parameter Dimension Reduction Methods Principal Components Regression Partial Least Squares Classification Logistic Regression The Logistic Model cost function Estimating the Coefficients Making Predictions Odds Ratio Performance Evaluation Matrices [Sensitivity/Specificity/PPV/NPV, Precision, ROC curve etc.] Multiple Logistic Regression Logistic Regression for >2 Response Classes Regularized Logistic Regression Linear Discriminant Analysis Using Bayes’ Theorem for Classification Linear Discriminant Analysis for p=1 Linear Discriminant Analysis for p >1 Quadratic Discriminant Analysis K-Nearest Neighbors Classification with Non-linear Decision Boundaries Support Vector Machines Optimization Objective The Maximal Margin Classifier Kernels One-Versus-One Classification One-Versus-All Classification Comparison of Classification Methods Introduction to Deep Learning ANN Structure Biological neurons and artificial neurons Non-linear Hypothesis Model Representation Examples & Intuitions Transfer Function/ Activation Functions Typical classes of network architectures Feed forward ANN. Structures of Multi-layer feed forward networks Back propagation algorithm Back propagation - training and convergence Functional approximation with back propagation Practical and design issues of back propagation learning Deep Learning Artificial Intelligence & Deep Learning Softmax Regression Self-Taught Learning Deep Networks Demos and Applications Lab: Getting Started with R Introduction to R Basic Commands & Libraries Data Manipulation Importing & Exporting data Graphical and Numerical Summaries Writing functions Regression Simple & Multiple Linear Regression Interaction Terms Non-linear Transformations Dummy variable regression Cross-Validation and the Bootstrap Subset selection methods Penalization [Ridge, Lasso, Elastic Net] Classification Logistic Regression, LDA, QDA, and KNN, Resampling & Regularization Support Vector Machine Resampling & Regularization Artificial Neural Network Deep Learning   Note: For ML algorithms, case studies will be used to discuss their application, advantages & potential issues. Analysis of different data sets will be performed using R
mlintro Introduction to Machine Learning 7 hours This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience. Naive Bayes Multinomial models Bayesian categorical data analysis Discriminant analysis Linear regression Logistic regression GLM EM Algorithm Mixed Models Additive Models Classification KNN Ridge regression Clustering
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
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking 21 hours DAY 1 - ARTIFICIAL NEURAL NETWORKS Introduction and ANN Structure. Biological neurons and artificial neurons. Model of an ANN. Activation functions used in ANNs. Typical classes of network architectures . Mathematical Foundations and Learning mechanisms. Re-visiting vector and matrix algebra. State-space concepts. Concepts of optimization. Error-correction learning. Memory-based learning. Hebbian learning. Competitive learning. Single layer perceptrons. Structure and learning of perceptrons. Pattern classifier - introduction and Bayes' classifiers. Perceptron as a pattern classifier. Perceptron convergence. Limitations of a perceptrons. Feedforward ANN. Structures of Multi-layer feedforward networks. Back propagation algorithm. Back propagation - training and convergence. Functional approximation with back propagation. Practical and design issues of back propagation learning. Radial Basis Function Networks. Pattern separability and interpolation. Regularization Theory. Regularization and RBF networks. RBF network design and training. Approximation properties of RBF. Competitive Learning and Self organizing ANN. General clustering procedures. Learning Vector Quantization (LVQ). Competitive learning algorithms and architectures. Self organizing feature maps. Properties of feature maps. Fuzzy Neural Networks. Neuro-fuzzy systems. Background of fuzzy sets and logic. Design of fuzzy stems. Design of fuzzy ANNs. Applications A few examples of Neural Network applications, their advantages and problems will be discussed. DAY -2 MACHINE LEARNING The PAC Learning Framework Guarantees for finite hypothesis set – consistent case Guarantees for finite hypothesis set – inconsistent case Generalities Deterministic cv. Stochastic scenarios Bayes error noise Estimation and approximation errors Model selection Radmeacher Complexity and VC – Dimension Bias - Variance tradeoff Regularisation Over-fitting Validation Support Vector Machines Kriging (Gaussian Process regression) PCA and Kernel PCA Self Organisation Maps (SOM) Kernel induced vector space Mercer Kernels and Kernel - induced similarity metrics Reinforcement Learning DAY 3 - DEEP LEARNING This will be taught in relation to the topics covered on Day 1 and Day 2 Logistic and Softmax Regression Sparse Autoencoders Vectorization, PCA and Whitening Self-Taught Learning Deep Networks Linear Decoders Convolution and Pooling Sparse Coding Independent Component Analysis Canonical Correlation Analysis Demos and Applications

Upcoming Courses

CourseCourse DateCourse Price [Remote / Classroom]
Metody Inteligencji Obliczeniowej - Wrocław, ul.Ludwika Rydygiera 2a/22Mon, 2017-06-12 09:006000PLN / 2018PLN
From Data to Decision with Big Data and Predictive Analytics - Opole, Władysława Reymonta 29Tue, 2017-06-13 09:0029220PLN / 10205PLN
From Zero to AI - Kielce, ul. Warszawska 19Mon, 2017-06-19 09:0024900PLN / 8545PLN

Other regions

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Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Python Programming Szczecin, ul. Sienna 9 Mon, 2017-05-29 09:00 10000PLN / 4448PLN
Facebook in advertising and marketing Lublin, ul. Spadochroniarzy 9 Fri, 2017-06-02 09:00 1881PLN / 1002PLN
MongoDB for Administrators Kraków, ul. Rzemieślnicza 1 Tue, 2017-06-06 09:00 3861PLN / 2087PLN
Oracle 11g - Programming with PL / SQL I - Workshops Wroclaw, ul.Ludwika Rydygiera 2a/22 Tue, 2017-06-06 09:00 5990PLN / 2939PLN
Adobe Photoshop Elements Gdynia, ul. Ejsmonda 2 Wed, 2017-06-07 09:00 1881PLN / 1127PLN
Microsoft Office Excel - moduł Business Intelligence Gdynia, ul. Ejsmonda 2 Wed, 2017-06-07 09:00 2673PLN / 1391PLN
Adobe Photoshop Elements Gdańsk, ul. Powstańców Warszawskich 45 Wed, 2017-06-07 09:00 1881PLN / 1127PLN
Adobe InDesign Poznan, Garbary 100/63 Thu, 2017-06-08 09:00 1881PLN / 1027PLN
Design Patterns in C# Poznan, Garbary 100/63 Thu, 2017-06-08 09:00 3861PLN / 1830PLN
SQL Fundamentals Gdańsk, ul. Powstańców Warszawskich 45 Thu, 2017-06-08 09:00 3663PLN / 1610PLN
Visual Basic for Applications (VBA) in Excel - Advanced Warszawa, ul. Złota 3/11 Mon, 2017-06-12 09:00 3069PLN / 1623PLN
Visual Basic for Applications (VBA) in Excel - Introduction to programming Gdynia, ul. Ejsmonda 2 Mon, 2017-06-12 09:00 3564PLN / 1891PLN
DTP (InDesign, Photoshop, Illustrator, Acrobat) Opole, Wladyslawa Reymonta 29 Mon, 2017-06-12 09:00 5940PLN / 4230PLN
Spring and Hibernate in Java Applications Poznan, Garbary 100/63 Tue, 2017-06-13 09:00 7722PLN / 3358PLN
Drools Rules Administration Wroclaw, ul.Ludwika Rydygiera 2a/22 Wed, 2017-06-14 09:00 21196PLN / 7023PLN
Adobe LiveCycle Designer Poznan, Garbary 100/63 Mon, 2017-06-19 09:00 2970PLN / 1885PLN
Build applications with Oracle Application Express (APEX) Katowice ul. Opolska 22 Mon, 2017-06-19 09:00 9801PLN / 4720PLN
Front End Developer Rzeszów, Plac Wolności 13 Mon, 2017-06-19 09:00 23000PLN / 7970PLN
Creating and managing Web sites Poznan, Garbary 100/63 Mon, 2017-06-19 09:00 5841PLN / 2298PLN
Introduction to Selenium Warszawa, ul. Złota 3/11 Thu, 2017-06-22 09:00 1871PLN / 824PLN
Javascript And Ajax Rzeszów, Plac Wolności 13 Mon, 2017-06-26 09:00 5841PLN / 3655PLN
Introduction to Programming Gdańsk, ul. Powstańców Warszawskich 45 Mon, 2017-06-26 09:00 5742PLN / 4121PLN
Implementation and Administration of Elasticsearch Wroclaw, ul.Ludwika Rydygiera 2a/22 Wed, 2017-06-28 09:00 20800PLN / 6903PLN
Effective interpersonal communication with elements of assertiveness Wroclaw, ul.Ludwika Rydygiera 2a/22 Thu, 2017-06-29 09:00 5148PLN / 1430PLN
Elasticsearch Advanced Administration, Monitoring and Maintenance Gdańsk, ul. Powstańców Warszawskich 45 Tue, 2017-07-04 09:00 17741PLN / 5876PLN
Nginx Setup, Configuration and Administration Bydgoszcz, ul. Dworcowa 94 Wed, 2017-07-05 09:00 6930PLN / 2850PLN
SQL Fundamentals Warszawa, ul. Złota 3/11 Mon, 2017-07-10 09:00 3663PLN / 1510PLN
SIP protocol in VoIP Poznan, Garbary 100/63 Mon, 2017-07-17 09:00 15929PLN / 5427PLN
Excel VBA Introduction Wroclaw, ul.Ludwika Rydygiera 2a/22 Wed, 2017-08-02 09:00 2376PLN / 1192PLN
Programming in WPF 4.5 Lublin, ul. Spadochroniarzy 9 Wed, 2017-08-16 09:00 6435PLN / 2443PLN
Creating and managing Web sites Poznan, Garbary 100/63 Mon, 2017-09-25 09:00 5841PLN / 2298PLN

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