Навучальныя курсы Machine Learning

This instructor-led, live training in Беларусь (online or onsite) is aimed at beginner to intermediate-level 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.

AlphaFold з'яўляецца Artificial Intelligence (AI) сістэмай, якая выконвае прагноз білкавых структураў. Афарызм (гр. aphorismos - выказванне) - выслоўе, у якім у трапнай, лаканічнай форме выказана значная і арыгінальная думка. Гэта інструктар-праведзены, жывы трэнінг (онлайн або на сайце) звязаны з біялогамі, якія хочуць разумець, як AlphaFold працуюць і выкарыстоўваюць AlphaFold мадэлі як гадавіны ў сваіх экспериментальных даследаваннях. У канцы гэтага трэніру ўдзельнікі зможаць:

Узнагароджанне асноўных принципаў AlphaFold. Узнагароджвайце, як гэта працуе. Узнікае пытанне: ці можа вера на самой справе змяніць свет?

Формат курса

Інтэрактыўныя лекцыі і дискусіі. Многія практыкаванні і практыкаванні. Вынікі ў Live-Lab Environment.

Вынікі пошуку - Customization options

Калі вы хочаце падзяліцца сваёй думкай з майстрам, рабіце гэта максімальна ветліва.

The Communist Theory of Law (1955) Электронная версія кнігі Гэта дае збор алгоритмаў машынабудавання для падрыхтоўкі дадзеных, класіфікацыі, класіфікацыі і іншых дадзеных мінеральных практыкаванняў. Гэта інструктар-праведзены, жывы трэнінг (онлайн або на сайце) накіраваны на аналітыкаў дадзеных і дадзеных навукоўцаў, якія хочуць выкарыстоўваць Weka для вывучэння задачы мінавання дадзеных. У канцы гэтага трэніру ўдзельнікі зможаць:

Здаровая касметыка вы можаце зрабіць самі Узнікае пытанне: ці можа вера на самой справе змяніць свет? Выконвайце задачы мінеральных дадзеных з дапамогай Weka.

Формат курса

Інтэрактыўныя лекцыі і дискусіі. Многія практыкаванні і практыкаванні. Вынікі ў Live-Lab Environment.

Вынікі пошуку - Customization options

Калі вы хочаце падзяліцца сваёй думкай з майстрам, рабіце гэта максімальна ветліва.

Дадатковыя функцыі ўключаюць у сябе джакузі для поўнай рэлаксацыі і камінам, каб трымаць вас у цяперашні час. За час работы ў школе Юлія Юр’еўна зразумела, што кожнае дзіця — асоба. Мы робім «Мост» другі год, але ўжо ведаем, што будзем рабіць, а што ня будзем рабіць у наступным годзе.

In this instructor-led, live training in Беларусь, 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.

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.

Гэта 4-дзённы курс, які ўводзіць AI і яе заяўку з дапамогай Python праграмнай мовы. У будучыні гэта можа стаць выдатным і паспяховым бізнесам.

Глыбіня Reinforcement Learning звяртаецца на магчымасць & quot; артыфікальнага агенту" навучэнне працэсу і памылкі і начар. Масавічны агент мае эмуляцыю чалавека ' зможнасці атрымаць і стварыць веды самае, несапраўдна з сурага ўводаў, як гляд. Для зразумевання падтрымкі навучэння, выкарыстоўваюцца глыбокі навучэнне і нерўныя сеткі. Перацягнуць вывучэнне аднакто ад машынаў, і не задаецца на наглядзе і непраглядзеныя падходы навучэння.У гэтым інструктарам, жывым вучэннем, удзельнікі будуць навучаць основы глыбокі Reinforcement Learning, калі яны працягнуць праз стварэнне Deep Learning Агента.Да канца гэтага прывучэння удзельнікі будуць магчыма:

Зразумець ключы канцепцыі за Глыбіня Reinforcement Learning і быць магчыма адрозненне яго ад Machine Learning Ужыць пашыраныя алгарытмы Reinforcement Learning для вырашэння праблемы рэальнага свету Пабудаваць Deep Learning Агент

Аўдыёмнасць

Распрацоўшчыкі навуковых дадзеных

Фармат курса

Часткая лекцыя, часткавыя працэсы, працэсы і цяжкія рукі на практыку

Машиннае навучанне — гэта філія штучнага разумення, у якой камп'ютары маюць магчымасць вучыцца без вызначанай праграмавання. Глыбокае навучанне - гэта падфільм машиннага навучання, які выкарыстоўвае методы, заснаваныя на выставе дадзеных і структурах навучання, такіх як нейральныя сеткі. Тлумачэнні ЦД парадку збора рэкрутаў з 5 і 25 дымоў............................................................................. У гэтым інструктар-праведзены, жывы трэнінг, удзельнікі навучаюцца, як ажыццяўляць мадэлі глыбокага навучання для тэлекам, выкарыстоўваючы Python як яны праходзяць па стварэнні мадэля глыбокага навучання крэдытнага ризику. У канцы гэтага трэніру ўдзельнікі зможаць:

Узнагароджанне фундаментальных концепцый глыбокага навучання. Узнагароджанне прыкладаў і прыкладаў глыбокага навучання ў тэлекоммунікацыі. Выкарыстоўвайце Python, Keras, і TensorFlow для стварэння мадэлей глыбокага навучання для тэлекоммунікацыі. Давайце пазнаёмімся з творчасцю з творчасцю з творчасцю з творчасцю з творчасцю з творчасцю з творчасцю з творчасцю з творчасцю.

Формат курса

Інтэрактыўныя лекцыі і дискусіі. Многія практыкаванні і практыкаванні. Вынікі ў Live-Lab Environment.

Вынікі пошуку - Customization options

Калі вы хочаце падзяліцца сваёй думкай з майстрам, рабіце гэта максімальна ветліва.

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

Гэта курс быў створаны для кіраўнікоў, архітэктораў рашэнняў, інновацыйных афіцый, ЦТР, архітэктораў праграмы і кожнага, хто зацікаўлены ў паглядзе прыкладнага штучнага разумення і найбліжэйшага прагнозу для яго развіцця.

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.

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.

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.

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.

This course will be a combination of theory and practical work with specific examples used throughout the event.

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.

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.

R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R  У Руднянскім бары размяшчалася база атрада «Дзімы», які вырас з разведвальна-дыверсійнай групы. R мае шырокую колькасць пакетаў для выкарыстання дадзеных.

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.

This course is created for people who have no previous experience in probability and statistics.

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.

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.

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

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

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

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 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

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

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