Machine Learning Training Courses

Machine Learning Training Courses

Local instructor-led live Machine Learning training courses in България.

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Machine Learning Course Outlines

Име на Kурса
Продължителност
Общ преглед
Име на Kурса
Продължителност
Общ преглед
7 hours
This instructor-led, live training in България (online or onsite) is aimed at software engineers or anyone who wish to learn how to use Vertex AI to perform and complete machine learning activities. By the end of this training, participants will be able to:
  • Understand how Vertex AI works and use it as a machine learning platform.
  • Learn about machine learning and NLP concepts.
  • Know how to train and deploy machine learning models using Vertex AI.
7 hours
AlphaFold е система, която извършва прогнозата на протеинови структури. Той е разработен от Alphabet’s/Google’s DeepMind като система за дълбоко учене, която може точно да предскаже 3D модели на протеинови структури. Това обучение, ръководено от инструктори, е насочено към биолози, които искат да разберат как AlphaFold работят и използват AlphaFold модели като ръководители в експерименталните си проучвания. В края на обучението участниците ще могат да:
    Разберете основните принципи на AlphaFold. Научете как действа AlphaFold. Научете как да тълкувате AlphaFold прогнози и резултати.
Формат на курса
    Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.
Опции за персонализиране на курса
    За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.
14 hours
Waikato Environment for Knowledge Analysis (Weka) е софтуер за визуализация за минни данни с отворен код. Той осигурява колекция от алгоритми за машинно обучение за подготовка на данни, класификация, кластериране и други дейности за извличане на данни. Това обучение, ръководено от инструктори, на живо (онлайн или онлайн) е насочено към аналитици на данни и учени на данни, които искат да използват Weka за изпълнение на задачи за изкопаване на данни. В края на обучението участниците ще могат да:
    Настройване и конфигуриране Weka Разбиране на Weka околната среда и работното място. Извършване на задачи за извличане на данни с помощта на Weka.
Формат на курса
    Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.
Опции за персонализиране на курса
    За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.
14 hours
Целта на този курс е да осигури основна компетентност в прилагането на Machine Learning методи в практиката. Чрез използването на Python програмния език и неговите различни библиотеки, и въз основа на множество практически примери този курс учи как да се използват най-важните строителни блокове на Machine Learning, как да се вземат решения за моделиране на данни, да се тълкуват резултатите от алгоритмите и да се валидират резултатите. Нашата цел е да ви предоставим уменията да разберете и използвате най-основните инструменти от Machine Learning инструменталната кутия с увереност и да избягвате често срещаните грешки в приложенията Data Science.
21 hours
In this instructor-led, live training in България, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data. By the end of this training, participants will be able to:
  • Implement machine learning algorithms and techniques for solving complex problems.
  • Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
  • Push Python algorithms to their maximum potential.
  • Use libraries and packages such as NumPy and Theano.
28 hours
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.
28 hours
Това е 4-дневен курс, който въвежда AI и прилагането му с помощта на Python програмния език. Има възможност да имате допълнителен ден, за да предприемете проект за ИИ при завършване на този курс. 
21 hours
Дълбокото Reinforcement Learning се свързва на способността на артификален агент" да научи чрез процедура и грешка и награда. Изкуството на цел да емулира човешко и#39; способност да получи и създаде познания, пряко от сирови входи като видение. За да се осъзнае подкреплението на учителство, се използва дълбоко учителство и неврални мрежи. Научването на преодоляването е различно от учителството на машини и не се обяви на надзора и не надгледащи подходи за учителство.В този инструктор, живо обучение, участниците ще научат основните на Дълбокото Reinforcement Learning, докато те стъпят през създаването на Deep Learning Агент.До края на този обучение участниците ще могат да:
    Разберете ключовите концепции зад Дълбокото Reinforcement Learning и можете да го разделите от Machine Learning Приложите напредни алгоритми Reinforcement Learning за решаване на проблемите на реално-святно изгради Deep Learning Агент
Слушателство
    Разработчиците данни научници
Формат на курса
    Частична лекция, частни дискусии, упражнения и тежки ръце на практика
28 hours
Машинното обучение е отрасъл на изкуствения интелект, в който компютрите имат способността да учат, без да бъдат изрично програмирани. Дълбокото обучение е подполе на машинното обучение, което използва методи, базирани на представления и структури на данни за учене, като например невронни мрежи. Python е високо ниво на програмиране език, известен със своята ясна синтеза и четене на кодове. В това обучение, ръководено от инструктори, участниците ще научат как да внедряват модели за дълбоко обучение за телекомуникации, като стъпват през създаването на модел за дълбоко обучение кредитен риск. В края на обучението участниците ще могат да:
    Разберете основните понятия за дълбоко учене. Научете приложенията и приложенията на дълбокото обучение в телекомуникациите. Използвайте Python, Keras и TensorFlow, за да създадете модели за дълбоко обучение за телекомуникации. Изградете своя собствен модел за предсказване на дълбокото обучение на клиента, като използвате Python.
Формат на курса
    Интерактивна лекция и дискусия. Много упражнения и упражнения. Изпълнение на ръката в живо лабораторна среда.
Опции за персонализиране на курса
    За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.
14 hours
Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow. This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project. By the end of this training, participants will be able to:
  • Explore how data is being interpreted by machine learning models
  • Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
  • Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
  • Explore the properties of a specific embedding to understand the behavior of a model
  • Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
Този курс е създаден за мениджъри, архитекти на решения, офицери за иновации, ЦТО, софтуерни архитекти и всеки, който се интересува от преглед на приложното изкуствено разузнаване и най-близката прогноза за неговото развитие.
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.
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  е свободен език за програмиране с отворен код за статистически компютри, анализ на данни и графики. Изследванията се използват от нарастващ брой мениджъри и аналитици на данни в корпорациите и академиите. R има широк спектър от пакети за извличане на данни.
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.
7 hours
The Wolfram System's integrated environment makes it an efficient tool for both analyzing and presenting data. This course covers aspects of the Wolfram Language relevant to analytics, including statistical computation, visualization, data import and export and automatic generation of reports.
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
  1. Investors and AI entrepreneurs
  2. Managers and Engineers whose company is venturing into AI space
  3. 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
21 hours
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
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

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Upcoming Machine Learning Courses

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