Szkolenia Machine Learning

Szkolenia Machine Learning

Machine Learning courses

Podkategorie

Plany Szkoleń Machine Learning

Identyfikator Nazwa Czas trwania (po 7h zegarowych dziennie) Przegląd
416995 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
463975 Machine Learning for Robotics 21 hours This course introduce machine learning methods in robotics applications. It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition. After short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software. Regression Probabilistic Graphical Models Boosting Kernel Methods Gaussian Processes Evaluation and Model Selection Sampling Methods Clustering CRFs Random Forests IVMs
417022 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
416996 Machine Learning Fundamentals with Python 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 Python programming language 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 Machine Learning with Python Choice of libraries Add-on tools 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
463700 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
810275 Machine Learning Fundamentals with Scala and Apache Spark 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 Scala programming language 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 Machine Learning with Python Choice of libraries Add-on tools 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
463718 Wprowadzenie do Neo4j - grafowej bazy danych 7 hours Wprowadzenie do Neo4j Instalacja i konfiguracja Struktura aplikacji Neo4j Relacyjne i grafowe sposoby reprezentacji danych Model grafowy danych Czy zagadnienie można i powinno reprezentować się jako graf? Wybrane przypadki użycia i modelowanie wybranego zagadnienia Najważniejsze pojęcia modelu grafowego Neo4j: Węzeł Relacja Właściwość Etykieta Język zapytań Cypher i operacje na grafach Tworzenie i zarządzanie schematem za pomocą języka Cypher Operacje CRUD na danych Zapytania Cypher oraz ich odpowiedniki w SQL Algorytmy grafowe wykorzystywane w Neo4j Interfejs REST Podstawowe zagadnienia administracyjne Tworzenie i odtwarzanie kopii zapasowych Zarządzanie bazą z poziomu przeglądarki Import i eksport danych w uniwersalnych formatach
915929 From Zero to AI 35 hours This course is created for people who have no previous expierence 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 Funcional Programming OOP Programming Exercises (writing logic for a game of choice, e.g. cross and noughts) 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
417023 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
751081 Advanced Deep Learning 28 hours Machine Learning Limitations Machine Learning, Non-linear mappings Neural Networks Non-Linear Optimization, Stochastic/MiniBatch Gradient Decent Back Propagation Deep Sparse Coding Sparse Autoencoders (SAE) Convolutional Neural Networks (CNNs) Successes: Descriptor Matching Stereo-based Obstacle Avoidance for Robotics Pooling and invariance Visualization/Deconvolutional Networks Recurrent Neural Networks (RNNs) and their optimizaiton Applications to NLP RNNs continued, Hessian-Free Optimization Language analysis: word/sentence vectors, parsing, sentiment analysis, etc. Probabilistic Graphical Models Hopfield Nets, Boltzmann machines, Restricted Boltzmann Machines Hopfield Networks, (Restricted) Bolzmann Machines Deep Belief Nets, Stacked RBMs Applications to NLP , Pose and Activity Recognition in Videos Recent Advances Large-Scale Learning Neural Turing Machines  
913005 Apache SystemML for Machine Learning 14 hours Apache SystemML is a distributed and declarative machine learning platform. SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark. Audience This course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning. Running SystemML Standalone Spark MLContext Spark Batch Hadoop Batch JMLC Tools Debugger IDE Troubleshooting Languages and ML Algorithms DML PyDML Algorithms
911901 Machine Learning with PredictionIO 21 hours PredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack. Audience This course is directed at developers and data scientists who want to create predictive engines for any machine learning task. Getting Started Quick Intro Installation Guide Downloading Template Deploying an Engine Customizing an Engine App Integration Overview Developing PredictionIO System Architecture Event Server Overview Collecting Data Learning DASE Implementing DASE Evaluation Overview Intellij IDEA Guide Scala API Machine Learning Education and Usage​ Examples Comics Recommendation Text Classification Community Contributed Demo Dimensionality Reducation and usage PredictionIO SDKs (Select One) Java PHP Python Ruby Community Contributed  
464009 Introduction to Machine Learning with MATLAB 21 hours MATLAB Basics MATLAB More Advanced Features BP Neural Network RBF, GRNN and PNN Neural Networks SOM Neural Networks Support Vector Machine, SVM Extreme Learning Machine, ELM Decision Trees and Random Forests Genetic Algorithm, GA Particle Swarm Optimization, PSO Ant Colony Algorithm, ACA Simulated Annealing, SA Dimenationality Reduction and Feature Selection

Kursy ze Zniżką

Szkolenie Miejscowość Data Kursu Cena szkolenia [Zdalne/Stacjonarne]
Administracja serwerem Apache Tomcat Kraków, ul. Rzemieślnicza 1 pon., 2016-10-03 09:00 2344PLN / 1736PLN
Programowanie w języku Scala Poznań, Garbary 100/63 wt., 2016-10-04 09:00 3479PLN / 2114PLN
Tworzenie aplikacji internetowych w języku PHP Szczecin, ul. Małopolska 23 wt., 2016-10-04 09:00 2688PLN / 2081PLN
Visual Basic for Applications (VBA) w Excel - wstęp do programowania Warszawa, ul. Złota 3/11 wt., 2016-10-04 09:00 1941PLN / 1504PLN
Wstęp do systemu Linux Poznań, Garbary 100/63 wt., 2016-10-04 09:00 1640PLN / 1100PLN
Docker for Developers and System Administrators Gdańsk, ul. Powstańców Warszawskich 45 śr., 2016-10-05 09:00 2507PLN / 1963PLN
Adobe InDesign Lublin, ul. Spadochroniarzy 9 śr., 2016-10-05 09:00 1220PLN / 1144PLN
ITIL® Foundation Certificate in IT Service Management Warszawa, ul. Złota 3/11 pon., 2016-10-10 09:00 2639PLN / 2076PLN
Visual Basic for Applications (VBA) w Excel - poziom zaawansowany Wrocław, ul.Ludwika Rydygiera 2a/22 pon., 2016-10-10 09:00 1689PLN / 1296PLN
Prognozowanie Rynku Poznań, Garbary 100/63 czw., 2016-10-13 09:00 2936PLN / 2112PLN
ITIL® Foundation Certificate in IT Service Management Łódź, ul. Tatrzańska 11 pon., 2016-10-17 09:00 2639PLN / 2160PLN
Microsoft Office Excel i Visual Basic for Applications (VBA) dla kontrolerów finansowych i audytorów Katowice ul. Opolska 22 pon., 2016-10-17 09:00 1941PLN / 1682PLN
Microsoft Office Excel - efektywna praca z arkuszem Rzeszów, Plac Wolności 13 wt., 2016-10-18 09:00 598PLN / 737PLN
Prognozowanie Rynku Warszawa, ul. Złota 3/11 śr., 2016-10-19 09:00 2936PLN / 2112PLN
ITIL® Foundation Certificate in IT Service Management Szczecin, ul. Małopolska 23 śr., 2016-10-19 09:00 2639PLN / 2134PLN
Podstawy inżynierii wymagań i analizy Gliwice ul. Karola Marksa 11 czw., 2016-10-20 09:00 2735PLN / 1967PLN
ITIL® Foundation Certificate in IT Service Management Zielona Góra, ul. Reja 6 pon., 2016-10-24 09:00 2639PLN / 2118PLN
Apache Tomcat and Java EE Administration Warszawa, ul. Złota 3/11 pon., 2016-10-24 09:00 2344PLN / 1555PLN
Wdrażanie efektywnych strategii cenowych Poznań, Garbary 100/63 śr., 2016-10-26 09:00 1427PLN / 1093PLN
Tworzenie i wygłaszanie prezentacji z Power Point (warsztat kompetencji społecznych) Poznań, Garbary 100/63 czw., 2016-10-27 09:00 1572PLN / 1121PLN
Agile Project Management with Scrum Kraków, ul. Rzemieślnicza 1 śr., 2016-11-02 09:00 1746PLN / 1449PLN
Adobe InDesign Lublin, ul. Spadochroniarzy 9 pon., 2016-11-07 09:00 1220PLN / 1144PLN
Agile Project Management with Scrum Poznań, Garbary 100/63 pon., 2016-11-07 09:00 1746PLN / 1315PLN
Administracja systemu Linux Olsztyn, ul. Kajki 3/1 wt., 2016-11-08 09:00 1940PLN / 1509PLN
SQL Fundamentals Warszawa, ul. Złota 3/11 śr., 2016-11-09 09:00 1358PLN / 853PLN
Microsoft Office Excel - analiza statystyczna Warszawa, ul. Złota 3/11 pon., 2016-11-14 09:00 1343PLN / 1031PLN
Visual Basic for Applications (VBA) w Excel - poziom zaawansowany Białystok, ul. Malmeda 1 pon., 2016-11-14 09:00 1689PLN / 1413PLN
Programowanie w języku Python Szczecin, ul. Małopolska 23 wt., 2016-11-15 09:00 5790PLN / 3824PLN
Tworzenie i zarządzanie stronami WWW Kraków, ul. Rzemieślnicza 1 pon., 2016-11-21 09:00 3410PLN / 2836PLN
Techniki graficzne (Adobe Photoshop, Adobe Illustrator) Wrocław, ul.Ludwika Rydygiera 2a/22 wt., 2016-12-06 09:00 1963PLN / 1470PLN

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