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Machine Learning for Data Science using MATLAB

Learn to implement classification and clustering algorithms using MATLAB with practical examples, projects and datasets

Description

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

  • K-Nearest Neighbor

  • Naïve Bayesain

  • 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 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

  • K-Means

  • Mean Shift

  • DBSCAN

  • Hierarchical Clustering

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

  • 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 categorical variable

Feature Scaling and Data Discretization


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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.

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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!

Best Regrads,

Dr. Nouman Azam

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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

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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.

Jeff Philips

★★★★★ 

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!

Oamar Kanji

★★★★★

The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!

Josh Nicassio

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)"

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