Szkolenia Machine Learning
Lokalne, prowadzone przez instruktorów szkolenia z zakresu uczenia maszynowego na żywo pokazują, jak zastosować praktyczne techniki uczenia maszynowego i narzędzia do rozwiązywania problemów w świecie rzeczywistym w różnych branżach Kursy NobleProg ML obejmują różne języki programowania i frameworki, w tym język Python, R i Matlab Kursy Machine Learning są oferowane dla szeregu aplikacji branżowych, w tym finansów, bankowości i ubezpieczeń, i obejmują podstawy uczenia maszynowego, a także bardziej zaawansowane podejścia, takie jak Deep Learning Szkolenie Machine Learning jest dostępne jako "szkolenie na miejscu" lub "szkolenie na żywo" Szkolenie na żywo w siedzibie klienta może odbywać się lokalnie w siedzibie klienta w Polsce lub w centrach szkoleniowych korporacji NobleProg w Polsce Zdalne szkolenie na żywo odbywa się za pomocą interaktywnego, zdalnego pulpitu NobleProg Twój lokalny dostawca szkoleń.
Opinie uczestników
Trener bardzo zrozumiale wytłumaczył trudne i zaawansowane tematy.
Leszek K
Nazwa kursu: Artificial Intelligence Overview
sposób prowadzenia i przykładay podawane przez trenera
ORANGE POLSKA S.A.
Nazwa kursu: Machine Learning and Deep Learning
Mozliwosc omowienia samemu zaproponowanych zagadnien
ORANGE POLSKA S.A.
Nazwa kursu: Machine Learning and Deep Learning
Wszystko co oceniłem na 6.
AB S.A.
Nazwa kursu: Machine Learning - szkolenie dedykowane
duża ilość informacji
Tomasz Szumigalski
Nazwa kursu: Machine Learning - szkolenie dedykowane
Jasno wyjaśnione zagadnienia
AB S.A.
Nazwa kursu: Machine Learning - szkolenie dedykowane
Duża wiedza trenera i zdolność do zrozumienia sensu naszych głupich pytań ;)
AB S.A.
Nazwa kursu: Machine Learning - szkolenie dedykowane
ref materiał do wykorzystania później był bardzo dobry.
PAUL BEALES- Seagate Technology.
Nazwa kursu: Applied Machine Learning
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Co najbardziej podobało Ci się w treningu ?: Dałem mi dobrą praktykę z użyciem R do budowania systemów uczenia maszynowego w rzeczywistych sytuacjach. Mogę użyć tego w mojej pracy od razu. What d
Nazwa kursu: Applied Machine Learning
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To było bardzo interaktywne i bardziej zrelaksowane i nieformalne niż się spodziewano. Omówiliśmy wiele tematów w tym czasie, a trener zawsze był otwarty na bardziej szczegółowe omówienie tematów i ich powiązanie. Uważam, że szkolenie dało mi narzędzia do kontynuacji nauki, a nie jednorazową sesję, w której nauka kończy się po ukończeniu, co jest bardzo ważne ze względu na skalę i złożoność tematu.
Jonathan Blease
Nazwa kursu: Artificial Neural Networks, Machine Learning, Deep Thinking
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ref materiał do wykorzystania później był bardzo dobry
PAUL BEALES
Nazwa kursu: Applied Machine Learning
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Trener był tak kompetentny i obejmował obszary, które mnie interesowały
Mohamed Salama
Nazwa kursu: Data Mining & Machine Learning with R
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Ogólny przegląd głębokiego uczenia się B
Bruno Charbonnier
Nazwa kursu: Advanced Deep Learning
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Ćwiczenia są wystarczająco praktyczne i nie wymagają dużej wiedzy w Pythonie. A
Alexandre GIRARD
Nazwa kursu: Advanced Deep Learning
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Wykonywanie ćwiczeń na prawdziwych przykładach za pomocą Keras. Mihaly całkowicie zrozumiał nasze oczekiwania dotyczące tego szkolenia. Pau
Paul Kassis
Nazwa kursu: Advanced Deep Learning
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Naprawdę doceniam krystalicznie czyste odpowiedzi Chrisa na nasze pytania.
Léo Dubus
Nazwa kursu: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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Doświadczony trener
Sridhar Voorakkara
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Byłem zdumiony standardem tej klasy, powiedziałbym, że był to standard uniwersytecki.
David Relihan
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Bardzo dobry przegląd ogólny. Dobre tło, dlaczego Tensorflow działa tak, jak działa. K
Kieran Conboy
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Spodobały mi się możliwości zadawania pytań i dokładniejszego wyjaśnienia teorii. Sha
Sharon Ruane
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Uzyskaliśmy o wiele więcej informacji na ten temat. Przeprowadzono kilka fajnych dyskusji z prawdziwymi tematami w naszej firmie
Sebastiaan Holman
Nazwa kursu: Machine Learning and Deep Learning
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Szkolenie zapewniło odpowiednią podstawę, dzięki której możemy dalej się rozwijać, pokazując, w jaki sposób teoria i praktyka idą w parze. To mnie bardziej zainteresowało w temacie niż przedtem.
Jean-Paul van Tillo
Nazwa kursu: Machine Learning and Deep Learning
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Pokrycie i głębia tematów
Anirban Basu
Nazwa kursu: Machine Learning and Deep Learning
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Bardzo elastyczny
Frank Ueltzhöffer
Nazwa kursu: Artificial Neural Networks, Machine Learning and Deep Thinking
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elastyczność
Werner Philipp
Nazwa kursu: Artificial Neural Networks, Machine Learning and Deep Thinking
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Gotowość do dzielenia się więcej
Balaram Chandra Paul
Nazwa kursu: A practical introduction to Data Analysis and Big Data
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Perspektywa technologii: jaka technologia / proces może stać się ważniejsza w przyszłości; zobacz, do czego można wykorzystać tę technologię Commer
Commerzbank AG
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Wybór tematu. Styl szkolenia. Poćwicz orientację
Commerzbank AG
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Obejmował szeroki zakres informacji.
Continental AG / Abteilung: CF IT Finance
Nazwa kursu: A practical introduction to Data Analysis and Big Data
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prezentacja technologii
Continental AG / Abteilung: CF IT Finance
Nazwa kursu: A practical introduction to Data Analysis and Big Data
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Wszyscy to lubią
蒙 李
Nazwa kursu: Machine Learning Fundamentals with Python
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Wszyscy to lubią
蒙 李
Nazwa kursu: Machine Learning Fundamentals with Python
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Ogólnie zawartość była dobra.
Sameer Rohadia
Nazwa kursu: A practical introduction to Data Analysis and Big Data
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Objaśnienie papieru
Nazwa kursu: Deep Learning with TensorFlow - bespoke
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Komunikacja z wykładowcami
文欣 张
Nazwa kursu: Artificial Neural Networks, Machine Learning, Deep Thinking
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Podoba się
lisa xie
Nazwa kursu: Artificial Neural Networks, Machine Learning, Deep Thinking
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Indepth pokrycie tematów uczenia maszynowego, w szczególności sieci neuronowe. Demistyfikował wiele tematów. S
Sacha Nandlall
Nazwa kursu: Python for Advanced Machine Learning
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Plany Kursów
| Kod | Nazwa | Czas trwania | Charakterystyka kursu |
|---|---|---|---|
| aiint | Artificial Intelligence Overview | 7 godz. | Kurs ten został stworzony dla menadżerów, architektów, analityków biznesowych i systemowych, menedżerów oprogramowania oraz wszystkich zainteresowanych przeglądem stosowania sztucznej inteligencji i prognozą dla jej rozwoju. |
| spmllib | Apache Spark MLlib | 35 godz. | MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs. It divides into two packages: spark.mllib contains the original API built on top of RDDs. spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines. Audience This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark |
| dmmlr | Data Mining & Machine Learning with R | 14 godz. | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| radvml | Advanced Machine Learning with R | 21 godz. | In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application. By the end of this training, participants will be able to: Use techniques as hyper-parameter tuning and deep learning Understand and implement unsupervised learning techniques Put a model into production for use in a larger application Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforbankingwithr | Deep Learning for Banking (with R) | 28 godz. | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use R to create deep learning models for banking Build their own deep learning credit risk model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| 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. |
| aiintrozero | From Zero to AI | 35 godz. | This course is created for people who have no previous experience in probability and statistics. |
| BigData_ | A Practical Introduction to Data Analysis and Big Data | 35 godz. | Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability. Audience Developers / programmers IT consultants Format of the course Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress. |
| encogintro | Encog: Introduction to Machine Learning | 14 godz. | Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored. By the end of this training, participants will be able to: Prepare data for neural networks using the normalization process Implement feed forward networks and propagation training methodologies Implement classification and regression tasks Model and train neural networks using Encog's GUI based workbench Integrate neural network support into real-world applications Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforfinancewithpython | Deep Learning for Finance (with Python) | 28 godz. | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use Python, Keras, and TensorFlow to create deep learning models for finance Build their own deep learning stock price prediction model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| 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. |
| singa | Mastering Apache SINGA | 21 godz. | SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning. Audience This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework. After completing this course, delegates will: understand SINGA’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, embedding terms, building graphs and logging |
| octnp | Octave nie tylko dla programistów | 21 godz. | Szkolenie dedykowane osobom, które chciałyby zapoznać się z obsługą programu alternatywnego do komercyjnego pakietu MATLAB. Kurs trzydniowy dostarcza kompleksowo informacje dotyczące poruszania się po środowisku i wykonywaniu pakietu OCTAVE w zastosowaniu do analizy danych i obliczeń inżynierskich. Adresatami szkolenia są osoby początkujące ale także ci, którzy znają program i chcieliby usystematyzować swoją wiedzę i podnieść umiejętności. Nie jest wymagana znajomość innych języków programowania ale w znacznym stopniu ułatwi to uczestnikom przyswajanie wiedzy. Na kursie pokazane zostaną możliwości wykorzystania program na wielu przykładach praktycznych. |
| encogadv | Encog: Advanced Machine Learning | 14 godz. | Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models. By the end of this training, participants will be able to: Implement different neural networks optimization techniques to resolve underfitting and overfitting Understand and choose from a number of neural network architectures Implement supervised feed forward and feedback networks Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| PaddlePaddle | PaddlePaddle | 21 godz. | PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu. In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications. By the end of this training, participants will be able to: Set up and configure PaddlePaddle Set up a Convolutional Neural Network (CNN) for image recognition and object detection Set up a Recurrent Neural Network (RNN) for sentiment analysis Set up deep learning on recommendation systems to help users find answers Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system. Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| 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. |
| caffe | Deep Learning for Vision with Caffe | 21 godz. | Caffe is a deep learning framework made with expression, speed, and modularity in mind. This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to: understand Caffe’s structure and deployment mechanisms carry out installation / production environment / architecture tasks and configuration assess code quality, perform debugging, monitoring implement advanced production like training models, implementing layers and logging |
| mlentre | Machine Learning Concepts for Entrepreneurs and Managers | 21 godz. | This training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same. Target Audience Investors and AI entrepreneurs Managers and Engineers whose company is venturing into AI space Business Analysts & Investors |
| pythontextml | Python: Machine Learning with Text | 21 godz. | In this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data. By the end of this training, participants will be able to: Solve text-based data science problems with high-quality, reusable code Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems Build effective machine learning models using text-based data Create a dataset and extract features from unstructured text Visualize data with Matplotlib Build and evaluate models to gain insight Troubleshoot text encoding errors Audience Developers Data Scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| cortana | Turning Data into Intelligent Action with Cortana Intelligence | 28 godz. | Cortana Intelligence Suite is a bundle of integrated products and services on the Microsoft Azure Cloud that enable entities to transform data into intelligent actions. In this instructor-led, live training, participants will learn how to use the components that are part of the Cortana Intelligence Suite to build data-driven intelligent applications. By the end of this training, participants will be able to: Learn how to use Cortana Intelligence Suite tools Acquire the latest knowledge of data management and analytics Use Cortana components to turn data into intelligent action Use Cortana to build applications from scratch and launch it on the cloud Audience Data scientists Programmers Developers Managers Architects Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| 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. |
| dlv | Deep Learning for Vision | 21 godz. | Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source ) for analyzing computer images This course provide working examples. |
| opennmt | OpenNMT: Setting up a Neural Machine Translation System | 7 godz. | OpenNMT is a full-featured, open-source (MIT) neural machine translation system that utilizes the Torch mathematical toolkit. In this training participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor. By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution. Source and target language samples will be pre-arranged per the audience's requirements. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice |
| mlios | Machine Learning on iOS | 14 godz. | In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they as they step through the creation and deployment of an iOS mobile app. By the end of this training, participants will be able to: Create a mobile app capable of image processing, text analysis and speech recognition Access pre-trained ML models for integration into iOS apps Create a custom ML model Add Siri Voice support to iOS apps Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| intelligentmobileapps | Building Intelligent Mobile Applications | 35 godz. | Intelligent applications are next generation apps that can continually learn from user interactions to provide better value and relevance to users. In this instructor-led, live training, participants will learn how to build intelligent mobile applications and bots. By the end of this training, participants will be able to: Understand the fundamental concepts of intelligent applications Learn how to use various tools for building intelligent applications Build intelligent applications using Azure, Cognitive Services API, Stream Analytics, and Xamarin Audience Developers Programmers Hobbyists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| annmldt | Artificial Neural Networks, Machine Learning, Deep Thinking | 21 godz. | Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML. |
| 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. |
| Fairsec | Fairsec: Setting up a CNN-based machine translation system | 7 godz. | Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice |
| tensorflowserving | TensorFlow Serving | 7 godz. | TensorFlow Serving is a system for serving machine learning (ML) models to production. In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment. By the end of this training, participants will be able to: Train, export and serve various TensorFlow models Test and deploy algorithms using a single architecture and set of APIs Extend TensorFlow Serving to serve other types of models beyond TensorFlow models Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| aifortelecom | AI Awareness for Telecom | 14 godz. | AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization. In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry. Audience Network engineers Network operations personnel Telecom technical managers Format of the course Part lecture, part discussion, hands-on exercises |
Upcoming Courses
| Szkolenie | Data Kursu | Cena szkolenia [Zdalne / Stacjonarne] |
|---|---|---|
| Facebook NMT: Setting up a Neural Machine Translation System - Białystok, ul. Malmeda 1 | wt., 2018-07-03 09:00 | 1660PLN / 2160PLN |
| Machine Learning and Deep Learning - Bydgoszcz, ul. Dworcowa 94 | wt., 2018-07-03 09:00 | 4990PLN / 5990PLN |
| Machine Learning Fundamentals with Scala and Apache Spark - Warszawa, ul. Złota 3/11 | wt., 2018-07-03 09:00 | 3330PLN / 4080PLN |
| OpenNMT: Setting up a Neural Machine Translation System - Katowice ul. Opolska 22 | wt., 2018-07-03 09:00 | 1660PLN / 2160PLN |
| Artificial Intelligence in Automotive - Olsztyn, ul. Gietkowska 6a | śr., 2018-07-04 09:00 | 3330PLN / 4080PLN |