Course description

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.

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 Beginner 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

Who this course is for:
  • Anyone looking to build a strong career in science or engineering through Excellent MATLAB coding skills
  • Anyone wanting to advance their skills of real world problem solving with MATLAB based scientific computing


Requirements:

  • We cover everything from scratch and therefore do not require any prior knowledge of MATLAB
  • The installation of MATLAB software on your machine is a must for this course so that you are able to run the commands and scripts that we cover during the course. If you do not have the MATLAB software installed than you may consider the following options
  • 1. You may download a free trail copy of the software from the MATHWORK website. This is for limited time use
  • 2. If you are student or employee, you may contact your School or employer for a free copy. Many universities offer a free student version of the software
  • 3. You may consider downloading the Octave which is a free and has nearly identical functionality as that of MATLAB. (I would not recommend this option since you may not be able to have access to all the functions that we cover in this course)
  • 4. If none of the above works for you, then you may purchase the student version directly from Mathworks website which is significantly lower in cost compare to its full version


What you'll learn:

  • Develop beginner to advance level skills of Programming with MATLAB. This is the only course which enables you to learn intermediate and advance programming data structures such as structures, tables, times tables, cells and map container.
  • Gain Hands-On experience with MATLAB for visualizing, analyzing and formulating intermediate and some advanced level problems using MATLAB programming skills
  • Experience some real world applications of MATLAB in solving Data Science problems.

Course curriculum

  • 1
    Instructor and Course Introduction
  • 2
    Handling Variables and Creating Scripts
    • Foundation for understanding variables
    • Different types of variables (strings, characters and logical)
    • Creating scripts and understanding commenting and semicolon effect
    • Data selection with the colon operator
  • 3
    Doing Basic Math in MATLAB
    • Basic math (addition, multiplication, subtraction and powers)
    • Understanding operation precedence
    • Computing GCD, LCM, permutations and prime numbers
    • Trigonometric functions
    • Set operations (union, intersection, complement and others)
    • Computing statistics of the matrices
    • Handling random numbers
    • Cross and dot product
    • Basic logical operations (and, or and not)
    • Sign and absolute functions
    • Converting numbers between different bases
    • Discretizing your data
  • 4
    Operations on Matrices
    • Computing unique elements
    • Determining membership of elements to a matrix
    • Shifting matrix elements
    • Determinant, inverse and diagonal elements
    • Relational operations
    • Commonly used matrices
    • Sorting matrix values
    • Size and length functions
    • Concatenating matrices
    • Finding non-zero elements
    • Frequency of values within a vector
  • 5
    Advanced Math Functions with Symbolic Data Types
    • Symbolic variables
    • Differentiation and integration using symbolic variables
    • Solving equations
    • Symbolic functions
  • 6
    Interacting with MATLAB and Graphics
    • Input output commands
    • More input output commands
    • Plotting data
    • Plotting 3-D data
    • More on plotting options
    • Combining plots with hold on
    • Interacting with the plot using the brush tool
    • Two y-axis on the same plot
    • Animated line
    • Bar graphs
    • Checking for existence of scripts, files, folders, variables or functions
    • Manipulating directory (part 1)
    • Manipulating directory (part 2)
    • Processing text files
  • 7
    Importing Data into MATLAB
    • Importing data from Excel into MATLAB
    • Importing data in different formats
    • Spreadsheet link (introduction and installation)
    • Passing data between Excel and MATLAB
    • Calling MATLAB functions from Excel
  • 8
    MATLAB Programming
    • Conditional if statements (part 1)
    • Conditional if statements (part 2)
    • For loops for iterating through your code
    • Nested for loops
    • While loops (when you don't know the number of iterations)
    • Breaking out from a loop before final condition
    • Continue statement for skipping an iteration
    • Switch statements for choice selection
  • 9
    Writing Your Own Functions
    • Creating custom built functions
    • Functions with inputs
    • Functions with multiple inputs and outputs
    • Returning from a function
  • 10
    Sharing Your Results
    • Sharing results with automatically generated reports
    • Sharing your results with live scripts
  • 11
    Cell Data Types
    • Creating and defining cells
    • Accessing data in a cell
    • Adding and deleting elements from a cell
    • Concatenating cells and passing cell contents to a function
  • 12
    Tables and Time Tables
    • Creating tables
    • Adding descriptions, units and accessing individual columns
    • Selecting and reordering rows
    • Sorting rows of a table
    • More properties of a table
    • Reading and writing tables to memory
    • Storing summary of a table
    • Adding and deleting rows from a table
    • Adding and deleting columns from a table
    • Dealing with missing data
    • Creating time tables
    • Properties, sorting and data selection in time tables
    • Concatenating time tables
    • Indexing and retrieving data based on row times
  • 13
    Working with Structures and Map Container Data Type
    • Creating structures
    • Retrieving data from a field of a structure
    • Concatenating structures
    • Storing data from a structure field into a variable
    • More operations on a structure
    • Creating map containers
    • Concatenation and more operations on map container
  • 14
    Converting Between Different Data Types
    • Converting other data types to cell
    • Converting cell to other data types
    • Converting from other to table data type
    • Converting from table to other data type

Take this course today!

"Machine Learning for Data Science using MATLAB"

Meet your instructor!

Nouman Azam
Your MATLAB Professor


I am Dr. Nouman Azam and i am Assistant Professor in Computer Science. I teach online courses related to MATLAB Programming to more than 10,000 students on different online platforms. 

The focus in these courses is to explain different aspects of MATLAB and how to use them effectively in routine daily life activities. In my courses, you will find topics such as MATLAB programming, designing gui's, data analysis and visualization. 

Machine learning techniques using MATLAB is one of my favorite topic. During my research career i explore the use of MATLAB in implementing machine learning techniques such as bioinformatics, text summarization, text categorization, email filtering, malware analysis, recommender systems and medical decision making.

Bundles including this course!