Course Overview
This course provides a comprehensive introduction to cybersecurity principles, practices, and tools used to protect networks, systems, and data from cyber threats. Learners will explore essential concepts including threat identification, risk management, ethical hacking, cryptography, malware, security operations, and compliance frameworks.
By the end of this course, participants will be able to:
- Identify and assess cybersecurity threats.
- Implement fundamental defensive security mechanisms.
- Understand the role of cryptography and network security.
- Apply ethical hacking and penetration testing methods.
Comply with legal, ethical, and regulatory standards in cybersecurity.

Requirements
- Python, Linear Algebra, Basic Probability
Target audiences
- students, professionals, IT administrators, or Anyone looking to build foundational knowledge in AI.
Curriculum
- 12 Sections
- 12 Lessons
- 70 Hours
Expand all sectionsCollapse all sections
- Introduction to AI & ML (4hours)2
- Python for AI & ML (4hours)1
- ML Algorithms & Concepts (6hours)1
- Deep Learning with TensorFlow (8hours)1
- PyTorch for Model Building (8hours)1
- Natural Language Processing (NLP) (8hours)1
- Computer Vision Applications (8hours)1
- AI in Healthcare (4hours)1
- AI in Fintech (4hours)1
- AI in Automation & IoT (4hours)1
- Ethics and AI Governance (4hours)1
- Capstone Project (10hours)1
This course is ideal for learners with basic knowledge of Python and math. Beginners can still join if they're willing to catch up.
No. Both are covered to ensure versatility and industry readiness.
Yes. You will use real-world datasets from healthcare, finance, and image/video sources.
Yes. A full module is dedicated to ethical use of AI and responsible development practices.
Mentors and instructors will guide you with feedback sessions and progress tracking.
You will work with Jupyter Notebooks, Google Colab, TensorFlow, PyTorch, scikit-learn, OpenCV, NLTK, and Hugging Face Transformers.