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:
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
MATLAB integrates computation, visualization and programming in an easy to use environment. It offers Financial Toolbox, which includes the features needed to perform mathematical and statistical analysis of financial data, then display the results with presentation-quality graphics.
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
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
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
