Machine Learning for Data Science using MATLAB
What you'll learn
- How to implement different machine learning classification algorithms using matlab.
- How to impplement different machine learning clustering algorithms using matlab.
- How to proprocess data before analysis.
- When and how to use dimensionality reduction.
- Take away code templates.
- Visualization results of algorithms
- Decide which algorithm to choose for your dataset
- MATLAB 2017a or heigher version. No prior knowledge of MATLAB is required
- In version below 2017a there might be some functions that will not work
Basic Course Description
This course is for you if you want to have a real feel of the Machine Learning techniques without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning theory but could never got a change or 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 very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. All the coding will be done in MATLAB which is one of the fundamental programming languages for engineer 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 course
In this section we spend some time talking about the topics you’ll learn, the approach of learning used in the course, essential details about MATLAB to get you started. This will give you an idea of what to expect from the course.
Segment 2: Data preprocessing (Brief videos)
We need to prepare and preprocess our data before applying Data Science algorithms and techniques. This section discusses the essential preprocessing techniques and discuses the topics such as getting rid of outliers, dealing with missing values, converting categorical data to numerical form, and feature scalling.
Segment 3: Classification Algorithms in MATLAB
Classification algorithms is an important class of Data Science algorithms and is a must learn for every data scientist. This section provides not only the intuition behind some of the most commonly used classification algorithm but also provides there implementation in MATLAB. The algorithms that we cover are
Support Vector Machine
In addition to these we also cover how to evaluate the performance of classifiers using different metrics.
Segment 4: Clustering Algorithms in MATLAB
This section introduces some of the commonly used clustering algorithms alongside with their intuition and implementation in MATLAB. We also cover the limitations of clustering algorithms by looking at their performance when the clusters are of different sizes, shapes and densities. The algorithms we cover in this section are
In the same section, we also cover practical application of the clustering algorithms by looking at the applications of image compression and sentence grouping. This section provides some intuition regarding the strengths of clustering in real life data analysis tasks.
Segment 5: Dimensionality Reduction
Dimensionality reduction is an important branch of algorithms in Data Science. In this section we show how to reduce the dimensions for a specific Data Science problems so that the visualization becomes easy. We cover the PCA algorithm in this section.
Segment 6: Project: Malware Analysis
In this section we provide a detailed project on malware analysis from one of our recent research paper. We provide introductory videos on how to complete the project. This will provide you with some hands on experience for analyzing Data Science problems.
Segment 7: Data preprocessing (Detailed Videos)
In this section we dive deep into the topic of data preprocessing and cover many interesting topics. The topic in this section include
Dealing with missing data using
Using mean and mode
Radom values for handling missing data
Class based strategies
Considering as a special value
Dealing with Categorical Variables using the
One hot encoding
Frequency based encoding
Target based encoding
Encoding in the presence of an order
Outlier Detection using
3 sigma rule with
Box plot rule
Histogram based rule
Local outlier factor
Outliers in categorical variable
Feature Scaling and Data Discretization
Your Benefits and Advantages:
If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked!
You will be sure of receiving quality contents since the instructors has already many courses in the MATLAB on udemy.
You have lifetime access to the course.
You have instant and free access to any updates i add to the course.
You have access to all Questions and discussions initiated by other students.
You will receive my support regarding any issues related to the course.
Check out the curriculum and Freely available lectures for a quick insight.
It's time to take Action!
Click the "Take This Course" button at the top right now!
...Time is limited and Every second of every day is valuable...
We are excited to see you in the course!
Dr. Nouman Azam
More Benefits and Advantages:
✔ You receive knowledge from an experienced instructor (Dr. Nouman Azam) who is the creator of five courses on Udemy in the MATLAB niche.
✔ The titles of these courses are
Complete MATLAB Tutorial: Go from Beginner to Pro
MATLAB App Desigining: The Ultimate Guide for MATLAB Apps
Go From Zero to Expert in Building Regular Expressions
Master Cluster Analys for Data Science using Python
Learn MATLAB Programming Skills while Solving Problems
Student Testimonials for Dr. Nouman Azam!
This is the second Udemy class on Matlab I've taken. Already, a couple important concepts have been discussed that weren't discussed in the previous course. I'm glad the instructor is comparing Matlab to Excel, which is the tool I've been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I'm delighted it covers complex numbers, derivatives, and integrals. I'm also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took.
Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!
The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!
Student Testimonials! who are also instructors in the MATLAB category
"Concepts are explained very well, Keep it up Sir...!!!"
Engr Muhammad Absar Ul Haq instructor of course "Matlab keystone skills for Mathematics (Matrices & Arrays)"
Who this course is for:
- Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
- Anyone who want to analyze the data
I am Dr. Nouman Azam and i am Associate Professor in Computer Science. I teach online courses related to MATLAB Programming and i have a rich community of students comprising of more than 25,000 students on different online plateforms.
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 techinques using MATLAB is one of my favourate 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.