Machine Learning Kurzusok

Machine Learning Kurzusok

A helyi, oktató által vezetett élő gépi tanulás (ML) képzések gyakorlati gyakorlattal mutatják be, hogyan alkalmazzák a gépi tanulási technikákat és eszközöket a különböző iparágakban a valós problémák megoldására. A NobleProg ML tanfolyamok különböző programozási nyelveket és kereteket tartalmaznak, beleértve a Python, R nyelv és a Matlab programokat. Gépi tanfolyamokat kínálnak számos ipari alkalmazáshoz, beleértve a pénzügyeket, bankokat és biztosításokat, és fedezik a gépi tanulás alapjait, valamint a fejlettebb megközelítéseket, mint például a Deep Learning. A gépi tanulás képzése "helyszíni élő képzés" vagy "távoli élő képzés" formájában érhető el. A helyszíni élő képzés helyi szinten valósulhat meg az ügyfél telephelyén Magyarország vagy a NobleProg vállalati oktatóközpontjaiban Magyarország . A távoli élőképzés interaktív, távoli asztal segítségével történik. NobleProg - a helyi oktatási szolgáltató

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Machine Learning (ML) Course Outlines

Kurzusnév
Időtartalma
Összefoglaló
Kurzusnév
Időtartalma
Összefoglaló
7 hours
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
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.
14 hours
This training course is for people that would like to apply Machine Learning in practical applications.

Audience

This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.

The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience.
14 hours
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
21 hours
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
This course will be a combination of theory and practical work with specific examples used throughout the event.
21 hours
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.
21 hours
MATLAB is a numerical computing environment and programming language developed by MathWorks.
14 hours
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
14 hours
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
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.
35 hours
This course is created for people who have no previous experience in probability and statistics.
21 hours
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
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
7 hours
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
14 hours
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
14 hours
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
14 hours
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
28 hours
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
14 hours
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
28 hours
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
28 hours
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
14 hours
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
14 hours
This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge
21 hours
This classroom based training session will explore machine learning tools with (suggested) Python. Delegates will have computer based examples and case study exercises to undertake.
14 hours
Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transforms. Knowledge of linear algebra helps engineers and developers improve their machine learning capabilities. Understanding linear algebra concepts allows them to better understand the principles behind machine learning techniques and thus solve problems faster.

In this instructor-led, live training, participants will learn the fundamentals of linear algebra as they step through solving a machine learning problem using linear algebra methods.

By the end of this training, participants will be able to:

- Understand fundamental linear algebra concepts
- Learn the linear algebra skills needed for machine learning
- Use linear algebra structures and concepts when working with data, images, algorithms, etc.
- Solve a machine learning problem using linear algebra

Audience

- Developers
- Engineers

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Note

- To request a customized training for this course, please contact us to arrange.
14 hours
What will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly?

In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining.

Audience

- City planners
- Architects
- Developers
- Transportation officials

Format of the Course

- Part lecture, part discussion, and a series of interactive exercises.

Note

- To request a customized training for this course, please contact us to arrange.
28 hours
This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
14 hours
This instructor-led, live training in Magyarország (online or onsite) is aimed at engineers who wish to apply feature engineering techniques to better process data and achieve obtain better machine learning models.

By the end of this training, participants will be able to:

- Set up an optimal development environment, including all needed Python packages.
- Obtain important insights by analyzing the features of a data set.
- Optimize machine learning models through adaptation of the raw data itself.
- Clean and transform data sets in preparation for machine learning.
21 hours
This instructor-led, live training in Magyarország (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems.

By the end of this training, participants will be able to:

- Gain an overview of artificial intelligence, machine learning, and computational intelligence.
- Understand the concepts of neural networks and different learning methods.
- Choose artificial intelligence approaches effectively for real-life problems.
- Implement AI applications in mechatronic engineering.

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Upcoming Machine Learning Courses

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Néhány ügyfelünk

is growing fast!

We are looking for a good mixture of IT and soft skills in Hungary!

As a NobleProg Trainer you will be responsible for:

  • delivering training and consultancy Worldwide
  • preparing training materials
  • creating new courses outlines
  • delivering consultancy
  • quality management

At the moment we are focusing on the following areas:

  • Statistic, Forecasting, Big Data Analysis, Data Mining, Evolution Alogrithm, Natural Language Processing, Machine Learning (recommender system, neural networks .etc...)
  • SOA, BPM, BPMN
  • Hibernate/Spring, Scala, Spark, jBPM, Drools
  • R, Python
  • Mobile Development (iOS, Android)
  • LAMP, Drupal, Mediawiki, Symfony, MEAN, jQuery
  • You need to have patience and ability to explain to non-technical people

To apply, please create your trainer-profile by going to the link below:

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