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Machine Learning Algorithms using MATLAB

Name: Machine Learning Algorithms using MATLAB
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Accelerate your Career

Learn machine learning algorithms with MATLAB. Explore regression, classification, clustering, and deployment for real-world AI applications.

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₹1,499

5 Modules

with Certifications

4:50 Hours

of Recorded Content

0 Ratings

by 0 Learners

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Paid Course

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1500 enrolled in this course

Course Specifications

Sections

Lessons

Capacity

Unlimited

Duration

4:50 Hours

Students

610

Access Duration

90 Days

Created Date

2 Jun 2026

Updated Date

4 Jun 2026

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Machine Learning Algorithms using MATLAB Course
Overview

This Explorer-level course provides a comprehensive introduction to machine learning algorithms using MATLAB, a widely used platform for data analysis, engineering, and scientific computing. Learners will begin with foundational predictive modeling techniques, including Linear Regression for prediction tasks and Logistic Regression for classification problems. The course then explores key classification algorithms such as Decision Trees, Support Vector Machines (SVM), Naïve Bayes, and k-Nearest Neighbors (kNN), enabling participants to solve a variety of real-world machine learning challenges. The curriculum further covers unsupervised learning techniques including K-Means Clustering and dimensionality reduction methods for extracting meaningful insights from complex datasets. Advanced ensemble learning approaches such as Random Forest, XGBoost, RobustBoost, RUSBoost, and LSBoost are introduced to improve model accuracy and performance. Learners will apply these concepts through hands-on exercises and a DNA Fragmentation Pattern Classification project, gaining practical experience in predictive analytics, pattern recognition, and intelligent decision-making systems. By the end of the course, participants will have the skills to develop, evaluate, and optimize machine learning models using MATLAB for engineering, research, and data science applications.

Key Features

Mentorship

Receive guidance and insights from industry experts

Hands-on Experience

Gain practical skills in a real-world cutting-edge projects.

Networking

Connect with professionals and peers in your field

Skill Development

Enhance your technical and soft skills

Career Advancement

Boost your resume with valuable experience

Dual Certificate

Get a certification to showcase your achievements

We are Accredited by

Our Awards & Achievements

Machine Learning Algorithms using MATLAB Course Roadmap

Module 1

Module 1 — Introduction to Machine Learning Models

Linear prediction using Linear regression

Logical classification using Logistic Regression

Module 2

Module 2 — Classification Algorithms

Making a decision using decision tree

Object classification using SVM - I

Object classification using SVM 2

Predictions using Naïve Bayes

Find out nearest neighbour using kNN

Module 3

Module 3 — Clustering and Dimensionality Reduction

Data clustering using K-Means

How to apply Dimensionality Reduction Algorithms?

Module 4

Module 4 — Ensemble Learning Techniques

How to apply Random Forest - I ?

How to apply Random Forest - II ?

Regression using RobustBoost - I

Regression using RobustBoost - II

Predictions using RUSBOOST - I

Predictions using RUSBOOST - II

Predictions using LSBoost - I

Predictions using LSBoost - II

Module 5

Module 5 — Classification and Pattern Analysis Projects

Project - DNA Fragmentation Pattern & Classification

After this Course, You will be Able to

Grasp FPGA architecture and microcontroller workings for advanced understanding
Build circuits, ranging from basic LEDs to intricate, sensor-driven projects
Master the HDL IDE, enabling proficient coding for real-world applications
Utilize breadboards and electronic components with precision for efficient circuitry
Create groundbreaking solutions by integrating sensors and motors innovatively
Explore diverse projects, from basic LEDs to complex motion-activated systems

Where Our Students Work

Our alumni are driving innovation at the world's most prestigious technology companies.

Learners Reviews

Aditi Sharma

The step by step approach from basics to real time FPGA projects made the concepts crystal clear. I especially loved the UART implementation—it gave me confidence to handle hardware communication protocols.

Rahul

This course bridges the gap between theory and practice. Programming the MAX10 FPGA board and controlling GPIOs gave me the kind of exposure that’s directly useful for internships and entry level roles in VLSI design.

Priya Nair

I came in with only basic digital electronics knowledge. The way Verilog was introduced and gradually applied to sequential circuits and FSMs was perfect. By the end, I could write and test my own HDL codes on Quartus.

Still Having Doubts?

1. Do I need prior experience?

No prior experience is required for foundational modules. Advanced topics may recommend basic knowledge in programming or electronics.

2. What if I face technical issues?

You can reach our support team at training@pantechelearning.com or call +91 89255 334 88 / 89.

3. What will I learn in this course?

You’ll cover: MATLAB basics for machine learning Regression techniques for prediction Classification algorithms for decision-making Clustering methods for pattern recognition Model deployment for real-world AI applications

4. How long is the course?

The program includes 5 structured modules, designed to be completed at your own pace, typically within 4–6 weeks.

5. Will I get a certificate?

Yes, upon completion you’ll receive a verified certification from Pantech eLearning, which can be shared on LinkedIn and with employers.

6. What is the course fee?

The Machine Learning Algorithms using MATLAB course is available for ₹1,499, inclusive of all modules and certification.

7. Is there any project work included?

Yes, you’ll apply your skills in a guided ML project, such as building regression or classification models, to reinforce concepts and demonstrate practical learning outcomes.

8. Can I access the course materials anytime?

Absolutely. Once enrolled, you’ll have lifetime access to the course videos, notes, and resources