TechSource Systems is MathWorks Authorised Reseller and Training Partner
Learn how to use MATLAB® and Statistics & Machine Learning Toolbox™ to perform statistical analysis with distribution fitting, regression, and hypothesis testing.
Complimentary Services: Post training email support & 1-hr consultation session within 1 month after the course completion!
Do you find yourself in an industry or field that increasingly uses data to answer questions? Are you working with an overwhelming amount of data and need to make sense of it? Do you want to avoid becoming a full-time software developer or statistician to do meaningful tasks with your data?
Data science uses scientific methods to gain useful information from data and apply knowledge from data over wide range of applications. In recent years, there are vast amount of data from many sources. The data needs to be processed to process to gain meaningful insight to build predictive model using complex machine learning algorithms.
Attend our 3-day Data Science with MATLAB MasterClass to learn the essential MATLAB hands-on skills you need to achieve practical results in Data Science quickly. Being able to visualize, analyze, and model data are some of the most in-demand career skills from fields ranging from healthcare to the auto industry, to tech startups.
This three-day course provides hands-on experience to perform statistical data analysis, machine learning techniques in MATLAB®, and practical deep learning using MATLAB®. The course examples and exercises demonstrate the use of appropriate MATLAB and Statistics and Machine Learning toolbox™ functionality throughout the analysis process; from importing and organizing data, to exploratory analysis, to confirmatory analysis and simulation. It demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. The introduction to practical deep learning using Deep Learning toolbox™ will be covered to provide attendees with additional selection of method to work with data. Topics include:
The course is intended for data scientists, engineers, researchers who take raw data and putting it in a form that can be used, examine its patterns, ranges, and biases to determine the usefulness of the information in predictive analysis using MATLAB.
MATLAB Fundamentals or equivalent experience using MATLAB®; and knowledge of basic statistics
Upon the completion of the course, the participants will be able to:
Objective: Bring data into MATLAB and organize it for analysis. Perform common tasks, such as merging data and dealing with missing data.
Objective: Perform basic statistical investigation of a data set, including visualization and calculation of summary statistics.
Objective: Investigate different probability distributions and fit distributions to a data set.
Objective: Simplify high-dimensional data sets by reducing the dimensionality.
Objective: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.
Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.
Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables.
Objective: Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.
Objective: Gain insight into how a network is operating by visualizing image data as it passes through the network. Apply this technique to different kinds of images.
Objective: Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.