MATLAB Training Courses

Динамичният анализ е процес на тестване и оценка на материал или програма по време на работа със софтуер. Това обучение, водено от инструктор, на живо (онлайн или на място) е насочено към начинаещи разработчици или инженери, които желаят да се научат как да използват цифрова симулация за динамични проблеми с помощта на Matlab. До края на това обучение участниците ще могат:

Разбиране на основите на динамичния анализ. Използвайте Matlab за извършване на аналитични и числени решения. Изведете уравнения за движение, като използвате различни подходи.

Формат на курса

Интерактивна лекция и дискусия. Много упражнения и упражнения. Практическо внедряване в жива лабораторна среда.

Опции за персонализиране на курса

За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организираме.

In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform.  Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles. In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic. In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation. Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation. Assessments will be conducted throughout the course to gauge progress. Format of the Course

• Course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation.

Note

• Practice sessions will be based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange.

This instructor-led, live training in България (online or onsite) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization. By the end of this training, participants will be able to:

• Install and configure a Python development environment.
• Understand the differences and similarities between Matlab and Python syntax.
• Use Python to obtain insights from various datasets. 
• Convert existing Matlab applications to Python. 
• Integrate Matlab and Python applications.

This two-day course provides a comprehensive introduction to the MATLAB® technical computing environment. The course is intended for beginner users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

MATLAB is a numerical computing environment and programming language developed by MathWorks.

This course contains a comprehensive material about MATLAB as a powerful simulation tool for communications. The aim of this course is to introduce MATLAB not only as a general programming language, rather, the role of the extremely powerful MATLAB capabilities as a simulation tool is emphasized. The examples given to illustrate the material of the course is not just a direct use of MATLAB commands, instead they often represent real problems.

A 3 day course that takes you through the MATLAB main screens and windows including ...

• how to use matlab as a caluclator and plot basic curves
• how to create your own customized functions and scripts

This four day course provides image processing foundations using Matlab. You will practise how to change and enhance images and even extract patterns from the images. You will also learn how to build 2D filters and apply them on the images. Examples and exercises demonstrate the use of appropriate Matlab and Image Processing Toolbox functionality throughout the analysis process.

Objective: This training is meant for software Engineers who are working with MBD technology, the training will cover Modelling techniques for Automotive systems, Automotive standards ,Auto-code generation and Model test harness building and verification Audience: Software developper for automotive supplier

Simulink is a graphical programming environment for modeling, simulating and analyzing multidomain dynamic systems.

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.

MATLAB интегрира изчисленията, визуализацията и програмирането в лесна за използване среда. Той предлага Financial Toolbox, който включва функциите, необходими за извършване на математически и статистически анализ на финансови данни, а след това показва резултатите с графики за представяне-качество. Това обучение, ръководено от инструктор, осигурява въведение в MATLAB за финансите. Ние се вливаме в анализ на данни, визуализация, моделиране и програмиране чрез практически упражнения и пълноценна лабораторна практика. Към края на това обучение, участниците ще имат задълбочено разбиране на мощните функции, включени в MATLAB's Financial Toolbox и ще са придобили необходимата практика, за да ги прилагат незабавно за решаване на проблеми в реалния свят. публиката

Финансови специалисти с предишен опит с MATLAB

Формат на курса

Частна лекция, частна дискусия, тежка практика

This course provides a comprehensive introduction to the MATLAB technical computing environment + an introduction to using MATLAB for financial applications. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:

• Working with the MATLAB user interface
• Entering commands and creating variables
• Analyzing vectors and matrices
• Visualizing vector and matrix data
• Working with data files
• Working with data types
• Automating commands with scripts
• Writing programs with logic and flow control
• Writing functions
• Using the Financial Toolbox for quantitative analysis

MATLAB е числена изчислителна среда и език за програмиране, разработен от MathWorks.

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events. In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data. By the end of this training, participants will be able to:

• Create predictive models to analyze patterns in historical and transactional data
• Use predictive modeling to identify risks and opportunities
• Build mathematical models that capture important trends
• Use data from devices and business systems to reduce waste, save time, or cut costs

Audience

• Developers
• Engineers
• Domain experts

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 Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to:

• Build a deep learning model
• Automate data labeling
• Work with models from Caffe and TensorFlow-Keras
• Train data using multiple GPUs, the cloud, or clusters

Audience

• Developers
• Engineers
• Domain experts

Format of the course

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

Prescriptive analytics is a branch of business analytics, together with descriptive and predictive analytics. It uses predictive models to suggest actions to take for optimal outcomes, relying on optimization and rules-based techniques as a basis for decision making. In this instructor-led, live training, participants will learn how to use Matlab to carry out prescriptive analytics on a set of sample data. By the end of this training, participants will be able to:

• Understand the key concepts and frameworks used in prescriptive analytics
• Use MATLAB and its toolboxes to acquire, clean and explore data
• Use rules-based techniques including inference engines, scorecards, and decision trees to make decisions based on different business scenarios
• Use Monte Carlo simulation to analyze uncertainties and ensure sound decision making
• Deploy predictive and prescriptive models to enterprise systems

Audience

• Business analysts
• Operations planners
• Functional managers
• BI (Business Intelligence) team members

Format of the course

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

This three-day course provides a comprehensive introduction to the MATLAB technical computing environment. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:

•     Working with the MATLAB user interface
•     Entering commands and creating variables
•     Analyzing vectors and matrices
•     Visualizing vector and matrix data
•     Working with data files
•     Working with data types
•     Automating commands with scripts
•     Writing programs with logic and flow control
•     Writing functions