Szkolenia Machine Learning

Szkolenia Machine Learning

Machine Learning courses

Podkategorie

Plany Szkoleń Machine Learning

Identyfikator Nazwa Czas trwania (po 7h zegarowych dziennie) Przegląd
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
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
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  
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
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
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
913005 Apache SystemML 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. ML algorithms are expressed in a R or Python syntax, that includes linear algebra primitives, statistical functions, and ML-specific constructs. This high-level language significantly increases the productivity of data scientists as it provides (1) full flexibility in expressing custom analytics, and (2) data independence from the underlying input formats and physical data representations. Automatic optimization according to data characteristics such as distribution on the disk file system, and sparsity as well as processing characteristics in the distributed environment like number of nodes, CPU, memory per node, ensures both efficiency and scalability.Running SystemML Standalone Spark MLContext Spark Batch Hadoop Batch JMLC Tools Debugger IDE Troubleshooting Languages and ML Algorithms DML PyDML Algorithms
911901 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  
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

Kursy ze Zniżką

Szkolenie Miejscowość Data Kursu Cena szkolenia [Zdalne/Stacjonarne]
Visual Basic for Applications (VBA) w Excel dla analityków Poznan, Garbary pon., 2016-07-04 09:00 1912PLN / 1278PLN
Debian Administration Poznan, Garbary pon., 2016-07-04 09:00 3157PLN / 2083PLN
Agile Project Management with Scrum Szczecin śr., 2016-07-06 09:00 1746PLN / 1361PLN
Wdrażanie efektywnych strategii cenowych Poznan, Garbary śr., 2016-07-06 09:00 1427PLN / 1093PLN
Język SQL w bazie danych MSSQL Poznan, Garbary czw., 2016-07-07 09:00 1568PLN / 1142PLN
Excel i VBA dla kontrolerów finansowych i audytorów Warszawa, ul. Złota 3/11 pon., 2016-07-11 09:00 1913PLN / 1441PLN
Introduction to Selenium Wrocław, ul.Ludwika Rydygiera 2a/22 czw., 2016-07-14 09:00 768PLN / 539PLN
Prognozowanie w R Poznan, Garbary czw., 2016-07-14 09:00 2188PLN / 1527PLN
Machine Learning Fundamentals with R Warszawa, ul. Złota 3/11 pon., 2016-07-18 09:00 2523PLN / 1828PLN
Administracja bazą danych Microsoft SQL Server 2012 Toruń, ul. Żeglarska 10/14 pon., 2016-07-18 09:00 2509PLN / 1653PLN
Administracja serwerem Apache Tomcat Kraków pon., 2016-07-18 09:00 1713PLN / 1438PLN
Efektywna praca z arkuszem Excel Gdańsk wt., 2016-07-19 09:00 768PLN / 652PLN
ITIL®: Intermediate Lifecycle Stream: Service Strategy (SS) Kraków wt., 2016-07-19 09:00 4338PLN / 3116PLN
Microsoft Access - pobieranie danych Poznan, Garbary śr., 2016-07-20 09:00 1117PLN / 856PLN
Debian Administration Olsztyn, ul. Kajki 3/1 pon., 2016-07-25 09:00 3157PLN / 2167PLN
Programowanie w języku Python Warszawa, ul. Złota 3/11 pon., 2016-08-01 09:00 5790PLN / 3753PLN
Oracle 11g - Analiza danych - warsztaty Warszawa, ul. Złota 3/11 pon., 2016-08-01 09:00 4350PLN / 3012PLN
Tworzenie aplikacji internetowych w języku PHP Warszawa, ul. Złota 3/11 śr., 2016-08-03 09:00 2688PLN / 2022PLN
Zapewnienie jakości oprogramowania – przegląd metodyk Warszawa, ul. Złota 3/11 wt., 2016-08-09 09:00 2735PLN / 1863PLN
Oracle 11g - Programowanie w PL/SQL II Wrocław, ul.Ludwika Rydygiera 2a/22 śr., 2016-08-10 09:00 2363PLN / 1785PLN
Trening radzenie sobie ze stresem Warszawa, ul. Złota 3/11 czw., 2016-08-18 09:00 2112PLN / 1212PLN
Java Spring Kraków pon., 2016-08-29 09:00 7039PLN / 5245PLN
Java Spring Szczecin pon., 2016-09-05 09:00 7039PLN / 5044PLN
Programowanie w WPF 4.5 Warszawa, ul. Złota 3/11 pon., 2016-09-05 09:00 2359PLN / 1355PLN
Building Web Apps using the MEAN stack Szczecin pon., 2016-09-12 09:00 4788PLN / 3124PLN
Java Spring Gdańsk pon., 2016-09-12 09:00 7039PLN / 5153PLN
Java Spring Poznan, Garbary pon., 2016-09-12 09:00 7039PLN / 4961PLN
Java Spring Wrocław, ul.Ludwika Rydygiera 2a/22 pon., 2016-09-19 09:00 7039PLN / 4961PLN
Java Spring Warszawa, ul. Złota 3/11 pon., 2016-09-19 09:00 7039PLN / 4961PLN
BPMN 2.0 dla Analityków Biznesowych Wrocław, ul.Ludwika Rydygiera 2a/22 wt., 2016-09-27 09:00 3110PLN / 2337PLN
Visual Basic for Applications (VBA) w Excel dla zaawansowanych Białystok, ul. Malmeda 1 pon., 2016-11-14 09:00 1689PLN / 1413PLN

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