What you'll get
- 11+ Hours
- 4 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Key ML concepts
- Implement ML algorithms in GNU Octave
- Visualize and analyze data using Octave plotting and scripting
- Apply optimization, regularization, and ensemble methods
- Evaluate, validate, and prepare models for real-world use
- Use Octave for practical ML tasks through hands-on exercises
- Master Octave basics
- Work with control flow
- Define functions and handle multiple return values
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Octave Machine Learning Training Basic | 3h 35m | ✔ | View Curriculum |
| Octave Machine Learning Training Intermediate | 2h 42m | ✔ | View Curriculum |
| Advanced Concepts of Octave Neural Network | 4h 31m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Octave Neural Network Fundamentals | 2h 02m | ✔ | View Curriculum |
Description
Learn machine learning from scratch using the free, open-source language GNU Octave. The course guides participants from beginner to advanced levels while combining theory with practical projects.
Section 1: Octave Machine Learning Training
Participants start with beginner-friendly lessons, learning core concepts such as linear and logistic regression and neural networks, and implementing these algorithms in Octave. The course then moves to intermediate topics, including support vector machines, clustering, and dimensionality reduction, with hands-on exercises and coding assignments to build real-world skills.
Project: Plotting and Scripts in Octave
Participants apply their skills in a practical project, learning to visualize data, create plots, and automate tasks using Octave scripts. This project helps reinforce concepts and gives practical experience in data analysis and visualization.
Section 2: Advanced GNU Octave Concepts
The advanced section covers topics like optimization algorithms, regularization techniques, and ensemble learning. Participants learn to fine-tune machine learning models, improve performance, and follow best practices for evaluation, validation, and deployment.
Throughout the course, learners participate in lectures, demonstrations, and hands-on projects to build the skills and confidence to use GNU Octave for machine learning, from data analysis to model optimization.
Requirements
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Basic computer knowledge
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Passion for learning
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Familiarity with basic coding terminology
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Access to a PC and the internet.
Target Audience
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Students interested in learning machine learning and Octave
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Professionals looking to enhance their data analysis or ML skills
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Anyone wanting to use GNU Octave for practical projects
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System administrators seeking scripting and automation skills
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School or college IT professors aiming to teach Octave and ML concepts.
