Lokala instruktörledda Live Machine Learning (ML) -kurser visar genom praktisk praktik hur man tillämpar maskininlärningstekniker och verktyg för att lösa verkliga problem i olika branscher. NobleProg ML kurser omfattar olika programmeringsspråk och ramverk, inklusive Python, R language och Matlab. Maskininlärningskurser erbjuds för ett antal industriapplikationer, däribland Finans, Bank och Försäkring, och täcker grunden för maskinlärning samt mer avancerade tillvägagångssätt som Deep Learning. Maskininlärningsträning är tillgänglig som "live-träning på plats" eller "fjärrsträning". Utbildning på plats kan genomföras lokalt på kundlokaler i Sverige eller i NobleProgs företagsutbildningscenter i Sverige . Fjärrutbildning sker genom en interaktiv fjärrskrivbord. NobleProg - Din lokala utbildningsleverantör
Machine Translated
Vittnesmål
Nercia Utbildning AB
Kurs: Deep Learning with TensorFlow 2.0
Machine Translated
Det var mycket interaktivt och mer avslappnat och informellt än förväntat. Vi omfattade många ämnen på tiden och tränaren var alltid mottaglig för att prata mer i detalj eller mer allmänt om ämnena och hur de var relaterade. Jag tycker att träningen har gett mig verktygen för att fortsätta att lära sig i motsats till att det är en engångsperiod där lärandet slutar när du är klar vilket är mycket viktigt med tanke på ämnets skala och komplexitet.
Jonathan Blease
Kurs: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Tränaren var så kunnig och inkluderade områden jag var intresserad av.
Mohamed Salama
Kurs: Data Mining & Machine Learning with R
Machine Translated
Ämnet är väldigt intressant.
Wojciech Baranowski
Kurs: Introduction to Deep Learning
Machine Translated
Tränare teoretisk kunskap och vilja att lösa problemen med deltagarna efter träningen.
Grzegorz Mianowski
Kurs: Introduction to Deep Learning
Machine Translated
Ämne. Mycket intressant!.
Piotr
Kurs: Introduction to Deep Learning
Machine Translated
Övningar efter varje ämne var verkligen hjälpsamma, trots att det var för komplicerat i slutet. I allmänhet var det presenterade materialet mycket intressant och involverat! Övningar med bildigenkänning var stora.
Dolby Poland Sp. z o.o.
Kurs: Introduction to Deep Learning
Machine Translated
Jag tror att om träning skulle ske på polska skulle det göra det möjligt för tränaren att dela sin kunskap effektivare.
Radek
Kurs: Introduction to Deep Learning
Machine Translated
Den globala översikten av djupt lärande.
Bruno Charbonnier
Kurs: Advanced Deep Learning
Machine Translated
Övningarna är tillräckligt praktiska och behöver inte ha hög kunskap i Python .
Alexandre GIRARD
Kurs: Advanced Deep Learning
Machine Translated
Göra övningar på riktiga exempel med hjälp av Eras. Italien förstod helt våra förväntningar om denna utbildning.
Paul Kassis
Kurs: Advanced Deep Learning
Machine Translated
Jag uppskattade verkligen kristallklara svaren på Chris till våra frågor.
Léo Dubus
Kurs: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
Jag har i allmänhet haft den kunniga tränaren.
Sridhar Voorakkara
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Jag var förvånad över standarden på den här klassen - jag skulle säga att det var universitetsstandard.
David Relihan
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Mycket bra översikt över hela världen. Go bakom bakgrund till varför Tensorflow fungerar som det gör.
Kieran Conboy
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Jag gillade möjligheterna att ställa frågor och få djupare förklaringar av teorin.
Sharon Ruane
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Vi har fått mycket mer inblick i ämnet. En del fina diskussioner gjordes med några riktiga ämnen inom vårt företag.
Sebastiaan Holman
Kurs: Machine Learning and Deep Learning
Machine Translated
Utbildningen gav rätt grund som gör att vi kan vidareutveckla oss genom att visa hur teori och praktik går hand i hand. Det fick mig faktiskt mer intresserad av ämnet än jag var tidigare.
Jean-Paul van Tillo
Kurs: Machine Learning and Deep Learning
Machine Translated
Jag har verkligen haft täckning och djup av ämnen.
Anirban Basu
Kurs: Machine Learning and Deep Learning
Machine Translated
Trainernas djupa kunskaper om ämnet.
Sebastian Görg
Kurs: Introduction to Deep Learning
Machine Translated
Mycket uppdaterat tillvägagångssätt eller KPI (tensorflöde, era, lär) att göra maskininlärning.
Paul Lee
Kurs: TensorFlow for Image Recognition
Machine Translated
Mycket flexibel.
Frank Ueltzhöffer
Kurs: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
Jag har i allmänhet haft flexibilitet.
Werner Philipp
Kurs: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
Med tanke på tekniken: vilken teknik / process kan bli viktigare i framtiden; se vad tekniken kan användas till.
Commerzbank AG
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Jag fick nytta av ämnesval. Stil av träning. Övningsorientering.
Commerzbank AG
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Fördjupad täckning av maskininlärning, särskilt neurala nätverk. Demystified mycket av ämnet.
Sacha Nandlall
Kurs: Python for Advanced Machine Learning
Machine Translated
Jag gillade verkligen övningar
L M ERICSSON LIMITED
Kurs: Machine Learning
Machine Translated
laboratoriet övar
Marcell Lorant - L M ERICSSON LIMITED
Kurs: Machine Learning
Machine Translated
Jupyter-anteckningsboken, där träningsmaterialet är tillgängligt
L M ERICSSON LIMITED
Kurs: Machine Learning
Machine Translated
Det fanns många övningar och intressanta ämnen.
L M ERICSSON LIMITED
Kurs: Machine Learning
Machine Translated
några bra labövningar analyseras och förklaras av tränaren i djupet (t.ex. covariants i linjär regression, som matchar den verkliga funktionen)
L M ERICSSON LIMITED
Kurs: Machine Learning
Machine Translated
Det är bara bra att allt material inklusive övningarna är på samma sida och då blir det uppdaterat på flygningen. Lösningen avslöjas i slutet. Häftigt! Jag uppskattar också att Krzysztof tog extra ansträngning för att förstå våra problem och föreslog oss möjliga tekniker.
Attila Nagy - L M ERICSSON LIMITED
Kurs: Machine Learning
Machine Translated
Jag var nytta av passionen att undervisa och fokusera på att göra saker förnuftiga.
Zaher Sharifi - GOSI
Kurs: Advanced Deep Learning
Machine Translated
Om ansikte område.
中移物联网
Kurs: Deep Learning for NLP (Natural Language Processing)
Machine Translated
De informella utbyten vi hade under föreläsningarna hjälpte mig verkligen att fördjupa min förståelse av ämnet
Explore
Kurs: Deep Reinforcement Learning with Python
Machine Translated
Det visar många metoder med förberedda skript - mycket snyggt förberedda material och lätt att spåra
Kamila Begej - GE Medical Systems Polska Sp. Zoo
Kurs: Machine Learning – Data science
Machine Translated
Jag gillar att utbildningen var fokuserad på exempel och kodning. Jag tänkte att det är omöjligt att packa så mycket innehåll i tre dagars träning, men jag tog fel. Träningen täckte många ämnen och allt gjordes på ett mycket detaljerat sätt (speciellt inställning av modellens parametrar - jag förväntade mig inte att det skulle finnas en tid för detta och jag blev överraskad överraskad).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Kurs: Machine Learning – Data science
Machine Translated
Jag gillar att det fokuserar mer på hur de olika metoderna för sammanfattning av text används
Kurs: Text Summarization with Python
Machine Translated
mycket information, alla svarade frågor, intressanta exempel
A1 Telekom Austria AG
Kurs: Deep Learning for Telecom (with Python)
Machine Translated
Tränaren var en professionell inom ämnesområdet och relaterad teori med tillämpning utmärkt
Fahad Malalla - Tatweer Petroleum
Kurs: Applied AI from Scratch in Python
Machine Translated
Jag började med nästan noll kunskap, och i slutet kunde jag bygga och utbilda mina egna nätverk.
Huawei Technologies Duesseldorf GmbH
Kurs: TensorFlow for Image Recognition
Machine Translated
Rock Solid Knowledge Ltd
Kurs: Machine Learning – Data science
Machine Translated
Richard Blewett - Rock Solid Knowledge Ltd
Kurs: Machine Learning – Data science
Machine Translated
Rock Solid Knowledge Ltd
Kurs: Machine Learning – Data science
Machine Translated
Justering till våra behov
Sumitomo Mitsui Finance and Leasing Company, Limited
Kurs: Kubeflow
Machine Translated
Ali Kattan - TWPI
Kurs: Natural Language Processing with TensorFlow
Machine Translated
Ibrahim Mohammedameen - TWPI
Kurs: Natural Language Processing with TensorFlow
Machine Translated
Usama Adam - TWPI
Kurs: Natural Language Processing with TensorFlow
Machine Translated
Tomasz känner verkligen till informationen väl och kursen var väl tempo.
Raju Krishnamurthy - Google
Kurs: TensorFlow Extended (TFX)
Machine Translated
Jag gillar att det fokuserar mer på hur de olika metoderna för sammanfattning av text används
Kurs: Text Summarization with Python
Machine Translated
Machine Learning (ML) underkategorier
Machine Learning läroplaner
Kurs Namn
Varaktighet
Översikt
Kurs Namn
Varaktighet
Översikt
7 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
This course will be a combination of theory and practical work with specific examples used throughout the event.
21 timmar
Översikt
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 timmar
Översikt
MATLAB is a numerical computing environment and programming language developed by MathWorks.
14 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
This course is created for people who have no previous experience in probability and statistics.
21 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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
28 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge
Kommande Machine Learning Kurser
16-08-2021 09:30
21 timmar
30-08-2021 09:30
35 timmar
09-09-2021 09:30
7 timmar
21-09-2021 09:30
21 timmar
21-09-2021 09:30
21 timmar
Andra länder
Konsult
Helg ML (Machine Learning) kurs, kvällMachine Learning utbildning, Machine Learning (ML) bootcamp, ML (Machine Learning) instruktörledd, Helg ML (Machine Learning) utbildning, Kväll ML (Machine Learning) kurs, Machine Learning coaching, Machine Learning instruktör, ML (Machine Learning) tränare, Machine Learning kurs, ML (Machine Learning) klasser, ML (Machine Learning) on-site, Machine Learning (ML) privata kurser, Machine Learning en till en utbildning
.jpg "procter & gamble (p&g)")
.png "european central bank (ecb)")
.png "european space agency (esa)")
.jpg "international air transport association (iata)")
.jpg "european southern observatory (eso)")
