Local, instructor-led live MATLAB training courses demonstrate through hands-on practice the fundamentals of MATLAB programming (syntax, arrays and matrices, data visualization, script development, object-oriented principles, etc.) as well as how to apply MATLAB's packages such as Financial Toolbox to perform mathematical and statistical analysis of financial data. MATLAB courses also include how to use related technologies such as Simulink to perform modeling of complex systems.
MATLAB training is available as "onsite live training" or "remote live training". Finland onsite live MATLAB trainings can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.
NobleProg -- Your Local Training Provider
Testimonials
Trainer took the initiative to cover additional content outside our course materials to improve our learning.
Chia Wu Tan - SMRT Trains Ltd
Course: MATLAB Programming
Exercises were most beneficent thing in the sessions
Halcon Systems
Course: MATLAB Programming
His deep knowledge about the subject
Course: MATLAB Fundamentals, Data Science & Report Generation
Teaching style and ability of the trainer to overcome unforeseen obstacles and adopt to circumstances. Broad knowledge and experience of the trainer
ASML
Course: Python for Matlab Users
Overall good intro to Python. The format of using Jupyter notebook and live examples on the projector was good for following along with the exercises.
ASML
Course: Python for Matlab Users
Hands on experience.
Matevz Nolimal - European Investment Bank
Course: MATLAB Programming
Many useful exercises, well explained
Helene Meadows - European Investment Bank
Course: MATLAB Programming
Students interact to solve problems
东风康明斯
Course: MATLAB Programming
Machine Translated
Interaction
chengyang cai - 东风康明斯
Course: MATLAB Programming
Machine Translated
Alternation theory / practice effective!
CIRAD
Course: Introduction to Machine Learning with MATLAB
Machine Translated
Progressive presentation and application of methods
Aurélien Briffaz - CIRAD
Course: Introduction to Machine Learning with MATLAB
Machine Translated
Availability and adaptability, answers to questions
Jean-Michel MEOT - CIRAD
Course: Introduction to Machine Learning with MATLAB
Machine Translated
Issues discussed, exercises carried out (examples), atmosphere of training, contact with the trainer, location.
Wojskowe Zakłady Uzbrojenia S.A. w Grudziądzu
Course: Octave not only for programmers
Machine Translated
His deep knowledge about the subject
Course: MATLAB Fundamentals, Data Science & Report Generation
MATLAB Course Outlines in Finland
- how to use matlab as a caluclator and plot basic curves
- how to create your own customized functions and scripts
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
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.
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
Examples and exercises demonstrate the use of appropriate Matlab and Image Processing Toolbox functionality throughout the analysis process.
- 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
This instructor-led training provides an introduction to MATLAB for finance. We dive into data analysis, visualization, modeling and programming by way of hands-on exercises and plentiful in-lab practice.
By the end of this training, participants will have a thorough understanding of the powerful features included in MATLAB's Financial Toolbox and will have gained the necessary practice to apply them immediately for solving real-world problems.
Audience
- Financial professionals with previous experience with MATLAB
Format of the course
- Part lecture, part discussion, heavy hands-on practice
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.
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
- 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
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