Machine Learning for Data Science using MATLAB

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 get a chance 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 scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to a bare minimum. All 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 worldwide. 

Below is a brief outline of this course:

Segment 1: Introduction to the course

In this section, we spend some time talking about the topics you’ll learn, the approach of learning used in the course, and 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 scaling.

Segment 3: Classification Algorithms in MATLAB

Classification algorithms are an important class of Data Science algorithms and are a must-learn for every data scientist. This section provides not only the intuition behind some of the most commonly used classification algorithms but also provides there implementation in MATLAB. 

The algorithms that we cover are:

• K-Nearest Neighbor

• Naïve Bayesian

• Support Vector Machine

• Decision Trees

• Discriminant Analysis

• Ensembles

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 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:

• K-Means

• Mean Shift

• DBSCAN

• Hierarchical Clustering

In the same section, we also cover the 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 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 papers. We provide introductory videos on how to complete the project. This will provide you with some hands-on experience in 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 topics in this section include:

• Dealing with missing data using

• Deleting strategies

• 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 a categorical variable

Feature Scaling and Data Discretization

Advantages of this course:

• If you do not find the course useful, you are covered with a 30-day money-back guarantee, full refund, no questions asked!

• You will be sure of receiving quality content since the instructors have already many courses in MATLAB on TutorialsPoint. 

• 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.

More Benefits and Advantages: 

• You receive knowledge from an experienced instructor (Dr. Nouman Azam) who is the creator of five courses on TutorialsPoint in the MATLAB niche. 

The titles of these courses are:

• Complete MATLAB Tutorial: Go from Beginner to Pro

• MATLAB App Designing: The Ultimate Guide for MATLAB Apps

• Go From Zero to Expert in Building Regular Expressions

• Master Cluster Analysis for Data Science using Python

• Learn MATLAB Programming Skills while Solving Problems

Who is this course for?

• Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
• Anyone who wants to analyze the data