Szkolenia Machine Learning

Szkolenia Machine Learning

Machine Learning courses

Opinie uczestników

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

Podkategorie

Plany Szkoleń Machine Learning

Kod Nazwa Czas trwania Przegląd
dladv Advanced Deep Learning 28 godz. 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  
predio Machine Learning with PredictionIO 21 godz. 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  
matlabml1 Introduction to Machine Learning with MATLAB 21 godz. 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
mlrobot1 Machine Learning for Robotics 21 godz. 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
wdneo4j Wprowadzenie do Neo4j - grafowej bazy danych 7 godz. 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
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking 21 godz. 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
mlintro Introduction to Machine Learning 7 godz. 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
mlfunpython Machine Learning Fundamentals with Python 14 godz. 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
MLFWR1 Machine Learning Fundamentals with R 14 godz. 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
aiauto Artificial Intelligence in Automotive 14 godz. 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        
aiintrozero From Zero to AI 35 godz. 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
mlfsas Machine Learning Fundamentals with Scala and Apache Spark 14 godz. 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
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example 28 godz. 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 o Inference o Loss o Training  Train the Model o The Graph o The Session o Train Loop  Evaluate the Model o Build the Eval Graph o 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
systemml Apache SystemML for Machine Learning 14 godz. 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
appliedml Applied Machine Learning 14 godz. 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

Najbliższe szkolenia

SzkolenieData KursuCena szkolenia [Zdalne / Stacjonarne]
Introduction to Machine Learning - Poznań, Garbary 100/63wt., 2017-02-07 09:005060PLN / 1883PLN
Apache SystemML for Machine Learning - Szczecin, ul. Małopolska 23wt., 2017-02-07 09:0016540PLN / 5512PLN
Advanced Deep Learning - Kielce, ul. Warszawska 19wt., 2017-02-07 09:0040890PLN / 13791PLN
Machine Learning Fundamentals with R - Opole, Władysława Reymonta 29wt., 2017-02-07 09:007000PLN / 4400PLN
From Zero to AI - Tarnów ul. Kościuszki 10 pon., 2017-02-20 09:0024900PLN / 9295PLN

Other regions

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Kursy w promocyjnej cenie

Szkolenie Miejscowość Data Kursu Cena szkolenia [Zdalne / Stacjonarne]
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Cassandra for Developers Łódź, ul. Tatrzańska 11 pon., 2017-02-27 09:00 17117PLN / 6087PLN
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Wprowadzenie do CSS3 Poznań, Garbary 100/63 śr., 2017-03-22 09:00 1881PLN / 952PLN

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