This course is for you if you want to fully equip yourself with the art of applied machine learning using MATLAB. This course is also for you if you want to apply the most commonly used data preprocessing techniques without having to learn all of the complicated math. Additionally, this course is also for you if you have taken previous machine learning implementation courses but could never figure out how to further improve the performance of the machine learning algorithms. By the end of this course, you will have at your fingertips, a vast variety of most commonly used data preprocessing techniques that you can use instantly to maximize your insight into your data set.
The approach in this course is very practical and we will build everything from scratch. We will immediately start coding after a couple of introductory tutorials and we will try to the theory to a minimum. All of the coding will be done in MATLAB, which is one of the fundamental programming languages for engineering and science students, and is frequently used by top data science research groups world wide.
Below is the brief outline of this course.
Segment 1: Introduction to the Course and MATLAB
Segment 2: Handling Missing Values
Segment 3: Dealing with Categorical Variables
Segment 4: Outlier Detection
Segment 5: Feature Scalling and Data Discretization
Segment 6: Project: Selecting Techniques for your Dataset
Dr. Nouman Azam is an Assistant Professor in Computer Science. He teaches 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 topics. 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.