Lokale, door instructeurs geleide Live Machine Learning (ML) -trainingscursussen demonstreren door hands-on de praktijk hoe technieken voor het leren van machines en hulpmiddelen voor het oplossen van problemen uit de echte wereld in verschillende industrieën kunnen worden toegepast. NobleProg ML-cursussen omvatten verschillende programmeertalen en -kaders, waaronder Python, R-taal en Matlab. Machine Learning-cursussen worden aangeboden voor een aantal bedrijfstoepassingen, waaronder financiën, bankieren en verzekeringen, en behandelen de fundamenten van machinaal leren, evenals geavanceerdere benaderingen zoals Deep Learning. Machine Learning-training is beschikbaar als 'live training op locatie' of 'live training op afstand'. Onsite live training kan lokaal worden uitgevoerd op het terrein van de klant in Nederland of in NobleProg bedrijfsopleidingscentra in Nederland . Live-training op afstand wordt uitgevoerd via een interactieve, externe desktop. NobleProg - uw lokale trainingsaanbieder
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Getuigenissen
We hebben veel meer inzicht gekregen in het onderwerp. Er is een aardige discussie gevoerd met enkele echte onderwerpen binnen ons bedrijf.
Sebastiaan Holman
Cursus: Machine Learning and Deep Learning
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De training bood de juiste basis om verder uit te breiden, door te laten zien hoe theorie en praktijk hand in hand gaan. Ik raakte hierdoor meer geïnteresseerd in het onderwerp dan voorheen.
Jean-Paul van Tillo
Cursus: Machine Learning and Deep Learning
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Ik heb echt genoten van de dekking en diepte van onderwerpen.
Anirban Basu
Cursus: Machine Learning and Deep Learning
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Het was heel interactief en meer ontspannen en informeel dan verwacht. We hebben veel onderwerpen behandeld in de tijd en de trainer was altijd ontvankelijk om meer in detail of meer in het algemeen te praten over de onderwerpen en hoe deze gerelateerd waren. Ik heb het gevoel dat de training me de tools heeft gegeven om verder te leren, in tegenstelling tot een eenmalige sessie waarbij het leren stopt als je klaar bent, wat erg belangrijk is gezien de schaal en complexiteit van het onderwerp.
Jonathan Blease
Cursus: Artificial Neural Networks, Machine Learning, Deep Thinking
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De trainer was zo deskundig en inclusief gebieden waarin ik geïnteresseerd was.
Mohamed Salama
Cursus: Data Mining & Machine Learning with R
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Het onderwerp is erg interessant.
Wojciech Baranowski
Cursus: Introduction to Deep Learning
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Train theoretische kennis en bereidheid om de problemen met de deelnemers na de training op te lossen.
Grzegorz Mianowski
Cursus: Introduction to Deep Learning
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Onderwerp. Heel interessant!.
Piotr
Cursus: Introduction to Deep Learning
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Oefeningen na elk onderwerp waren erg nuttig, ondanks dat er aan het eind te gecompliceerd was. Over het algemeen was het gepresenteerde materiaal erg interessant en betekend het! Oefeningen met beeldherkenning waren geweldig.
Dolby Poland Sp. z o.o.
Cursus: Introduction to Deep Learning
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Ik denk dat als de training in het Pools wordt gedaan, de trainer zijn kennis efficiënter zou kunnen delen.
Radek
Cursus: Introduction to Deep Learning
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Het wereldwijde overzicht van deep learning.
Bruno Charbonnier
Cursus: Advanced Deep Learning
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De oefeningen zijn voldoende praktisch en hebben geen hoge kennis van Python nodig om gedaan te worden.
Alexandre GIRARD
Cursus: Advanced Deep Learning
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Oefeningen doen op echte voorbeelden met behulp van Eras. Italië heeft onze verwachtingen over deze training volledig begrepen.
Paul Kassis
Cursus: Advanced Deep Learning
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Ik waardeerde echt de kristalheldere antwoorden van Chris op onze vragen.
Léo Dubus
Cursus: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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Ik genoot over het algemeen van de deskundige trainer.
Sridhar Voorakkara
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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Ik was verbaasd over de standaard van deze klas - ik zou zeggen dat het een universitaire standaard was.
David Relihan
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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Zeer goed overzicht. Go achtergrond waarom Tensorflow zo werkt.
Kieran Conboy
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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Ik vond de mogelijkheden om vragen te stellen en meer diepgaande verklaringen van de theorie te krijgen.
Sharon Ruane
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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De trainer legde zeer gemakkelijk moeilijke en geavanceerde onderwerpen uit.
Leszek K
Cursus: Artificial Intelligence Overview
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De diepe kennis van de trainer over het onderwerp.
Sebastian Görg
Cursus: Introduction to Deep Learning
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Zeer geactualiseerde aanpak of CPI (tensor flow, era, learn) om machine learning te doen.
Paul Lee
Cursus: TensorFlow for Image Recognition
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Zeer flexibel.
Frank Ueltzhöffer
Cursus: Artificial Neural Networks, Machine Learning and Deep Thinking
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Ik genoot over het algemeen van de flexibiliteit.
Werner Philipp
Cursus: Artificial Neural Networks, Machine Learning and Deep Thinking
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Gezien de visie van de technologie: welke technologie / proces kan in de toekomst belangrijker worden; zie, waar de technologie voor kan worden gebruikt.
Commerzbank AG
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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Ik had baat bij het selecteren van onderwerpen. Stijl van training. Oefen oriëntatie.
Commerzbank AG
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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Het bevalt allemaal
蒙 李
Cursus: Machine Learning Fundamentals with Python
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manier van leidinggeven en voorbeeld gegeven door de trainer
ORANGE POLSKA S.A.
Cursus: Machine Learning and Deep Learning
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Mogelijkheid om de voorgestelde problemen zelf te bespreken
ORANGE POLSKA S.A.
Cursus: Machine Learning and Deep Learning
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Communicatie met docenten
文欣 张
Cursus: Artificial Neural Networks, Machine Learning, Deep Thinking
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Vind het leuk
lisa xie
Cursus: Artificial Neural Networks, Machine Learning, Deep Thinking
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Diepgaande aandacht voor onderwerpen van machine learning, met name neurale netwerken. Demystified veel van het onderwerp.
Sacha Nandlall
Cursus: Python for Advanced Machine Learning
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Ik hield echt van oefeningen
L M ERICSSON LIMITED
Cursus: Machine Learning
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de practica
Marcell Lorant - L M ERICSSON LIMITED
Cursus: Machine Learning
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Het Jupyter-notitieblok, waarin het trainingsmateriaal beschikbaar is
L M ERICSSON LIMITED
Cursus: Machine Learning
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Er waren veel oefeningen en interessante onderwerpen.
L M ERICSSON LIMITED
Cursus: Machine Learning
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enkele geweldige laboefeningen geanalyseerd en uitgelegd door de trainer in de diepte (bijv. covarianten in lineaire regressie, passend bij de echte functie)
L M ERICSSON LIMITED
Cursus: Machine Learning
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Het is gewoon geweldig dat al het materiaal, inclusief de oefeningen, op dezelfde pagina staat en dan wordt het on-the-fly bijgewerkt. De oplossing wordt aan het einde onthuld. Koel! Ook stel ik op prijs dat Krzysztof extra moeite heeft gedaan om onze problemen te begrijpen en ons mogelijke technieken heeft voorgesteld.
Attila Nagy - L M ERICSSON LIMITED
Cursus: Machine Learning
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Grote en up-to-date kennis van toonaangevende en praktische toepassingsvoorbeelden.
ING Bank Śląski S.A.
Cursus: Introduction to Deep Learning
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Veel oefeningen, zeer goede samenwerking met de groep.
Janusz Chrobot - ING Bank Śląski S.A.
Cursus: Introduction to Deep Learning
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werk aan colaborators,
ING Bank Śląski S.A.
Cursus: Introduction to Deep Learning
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Het was duidelijk dat de liefhebbers van de gepresenteerde onderwerpen leidend waren. Gebruikte interessante voorbeelden tijdens het sporten.
ING Bank Śląski S.A.
Cursus: Introduction to Deep Learning
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Een breed scala aan onderwerpen behandeld en substantiële kennis van de leiders.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Cursus: Understanding Deep Neural Networks
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gebrek
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Cursus: Understanding Deep Neural Networks
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Grote theoretische en praktische kennis van de docenten. Communicativiteit van trainers. Tijdens de cursus kun je vragen stellen en bevredigende antwoorden krijgen.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Cursus: Understanding Deep Neural Networks
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Praktisch gedeelte, waar we algoritmen hebben geïmplementeerd. Dit zorgde voor een beter begrip van het onderwerp.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Cursus: Understanding Deep Neural Networks
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oefeningen en voorbeelden geïmplementeerd op hen
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Cursus: Understanding Deep Neural Networks
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Voorbeelden en problemen besproken.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Cursus: Understanding Deep Neural Networks
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Machine Learning subcategorieën
Machine Learning (ML) Cursusoverzicht
Course Name
Duration
Overview
Course Name
Duration
Overview
7 hours
Overview
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
7 hours
Overview
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.
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.
14 hours
Overview
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.
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.
14 hours
Overview
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.
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.
14 hours
Overview
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.
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.
21 hours
Overview
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.
21 hours
Overview
This course will be a combination of theory and practical work with specific examples used throughout the event.
21 hours
Overview
This course introduces 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 a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition.
After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
21 hours
Overview
MATLAB is a numerical computing environment and programming language developed by MathWorks.
14 hours
Overview
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.
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.
14 hours
Overview
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.
21 hours
Overview
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.
Audience
This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
35 hours
Overview
This course is created for people who have no previous experience in probability and statistics.
21 hours
Overview
Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.
21 hours
Overview
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
Target Audience
- Investors and AI entrepreneurs
- Managers and Engineers whose company is venturing into AI space
- Business Analysts & Investors
7 hours
Overview
Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.
In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.
By the end of this training, participants will be able to:
- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.
By the end of this training, participants will be able to:
- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
The aim of this course is to provide general 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.
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.
14 hours
Overview
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
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
14 hours
Overview
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
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
21 hours
Overview
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
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
14 hours
Overview
In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack 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
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
28 hours
Overview
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects.
Audience
- Developers
- Data scientists
- Banking professionals with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects.
Audience
- Developers
- Data scientists
- Banking professionals with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.
In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.
By the end of this training, participants will be able to:
- Install and configure OpenNLP
- Download existing models as well as create their own
- Train the models on various sets of sample data
- Integrate OpenNLP with existing Java applications
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.
By the end of this training, participants will be able to:
- Install and configure OpenNLP
- Download existing models as well as create their own
- Train the models on various sets of sample data
- Integrate OpenNLP with existing Java applications
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.
In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.
By the end of this training, participants will be able to:
- Use a command-line tool that summarizes text.
- Design and create Text Summarization code using Python libraries.
- Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.
By the end of this training, participants will be able to:
- Use a command-line tool that summarizes text.
- Design and create Text Summarization code using Python libraries.
- Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. 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 apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
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
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
14 hours
Overview
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
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
14 hours
Overview
This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge
Aankomende Machine Learning (ML) cursussen
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Amsterdam
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