This course is for you if you want to learn Machine Learning techniques without having to learn all of the complicated math. Additionally, this course is also for you if you have had previous classes in machine learning theory but could never figure out how to implement and solve data science problems with it.
The approach in this course is very practical and we will start everything from scratch. We will immediately start coding after a couple of introductory tutorials and we will try to keep the theory to the bare 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 a brief outline of the course.
Segment 1: Introduction to Course
Segment 2: Data Preprocessing
Segment 3: Classification Algorithms in MATLAB
Segment 4: Clustering Algorithms in MATLAB
Segment 5: Dimensionality Reduction
Segment 6: Project: Malware Analysis
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.
StartSource Code and Data
StartSection introduction (1:54)
StartImporting the data into MATLAB (7:25)
StartHandling missing data (part 1) (7:43)
StartHandling missing data (part 2) (6:46)
StartFeature scaling (9:50)
StartHandling outliers (part 1) (9:07)
StartHandling outliers (part 2) (6:02)
StartDealing with categorical data (part 1) (9:50)
StartDealing with categorical data (part 2) (6:20)
StartYour data preprocessing template (3:58)