MATLAB Master Class Go from Beginner to Expert in MATLAB

Basic Course Description 

MATLAB (matrix laboratory) is one of the fundamental and leading programming language and is a must learn skill for anyone who want to develop a career in engineering, science or related fields. Excellent MATLAB programming skills is therefore a crucial factor in making or breaking your career.

This course is designed from a perspective of a student who has no prior knowledge of MATLAB. The course starts from the very basic concepts and then built on top of those basic concepts and move towards more advanced topics such as visualization, exporting and importing of data, advance data types and data structures and advance programming constructs.

To get the real feel of MATLAB in solving and analyzing real life problems, the course includes machine learning topics in data science and data preprocessing. To convert the source codes into meaningful pieces of softwares, the course also covers topics in building GUI’s using GUIDE and App Designer utilities of matlab. Finally, we also cover topics in text processing such as building Regular Expressions.

The course is fun and exciting, but at the same time we dive deep into MATLAB to uncover its power of formulating and analyzing real life problems. The course is structured into four different Parts. Below is the detailed outline of this course.

Part 1: MATLAB from Beginer to Advance 

Segment 1.1: Handling variables and Creating Scripts

Segment 1.2: Doing Basic Maths in MATLAB

Segment 1.3: Operations on Matrices

Segment 1.4: Advance Math Functions with Symbolic Data Type

Segment 1.5: Interacting with MATLAB and Graphics

Segment 1.6: Importing Data into MATLAB

Segment 1.7: File Handling and Text Processing

Segment 1.8: MATLAB Programming

Segment 1.9: Sharing Your MATLAB Results

Part 2: Advance MATLAB Data Types

Segment 2.1: Cell Data Type

Segment 2.2: Tables and Time Tables

Segment 2.3: Working with Structures and Map Container Data Type

Segment 2.4: Converting between Different Data Types

Part 3: Machine Learning for Data Science Using MATLAB

Segment 3.1 Data Preprocessing

Segment 3.2. Classification

Segment 3.2.1 K-Nearest Neighbor

Segment 3.2.2 Naive Bayes

Segment 3.2.3 Decision Trees

Segment 3.2.4 Support Vector Machine

Segment 3.2.5 Discriminant Analysis

Segment 3.2.6 Ensembles

Segment 3.2.7 Performance Evaluation

Segment 3.3 Clustering

Segment 3.3.1 K-Means

Segment 3.3.2 Hierarchical Clu stering

Segment 3.4 Dimensionality Reduction

Segment 3.5 Project

Part 4: Data Preprocessing for Machine Learning using MATLAB

Segment 4.1 Handing Missing Values

Segment 4.2 Dealing with Categorical Variables

Segment 4.3  Outlier Detection

Segment 4.4 Feature Scaling and Data Discretization

Segment 4.5 Selecting the Right Method for your Data

Part 5: Build Regular Expression using MATLAB for Text Processing

Segment 5.1 : Introduction and getting started with regexes

Segment 5.2: Character Classes

Segment 5.3: Anchors and Word Boundaries

Segment 5.4: Repetitiongs using Quantifiers

Segment 5.5: Group Constructs

Segment 5.6: Assertions, Conditions and Backreferencing

Segment 5.7: Practical Examples

Part 6: Matlab App Designing Using Guide

Segment 6.1: Basics of the GUIDE

Segment 6.2: Linking the Code with GUI

Segment 6.3: Advance Techniques for GUIDE

Segment 6.4: Sample Projects with GUIDE

Segment 6.5: More Useful Tricks and Examples with GUIDE

Part 7: Create MATLAB Apps with App Designer

Segment 7.1: Basics of App Designer

Segment 7.2: Tips and Tricks for Effective use of App Designer

Segment 7.3: Coding GUI’s

Segment 7.4: Advance Techniques

Segment 7.5: Sample Projects with App Designer