ABOUT COURSE

The Online Advanced Certification in Machine Learning & Cyber Security is a cutting-edge program designed to provide in-depth knowledge and hands-on experience in key areas of AI, cybersecurity, automata theory, and NLP. This course blends theoretical foundations with real-world applications, ensuring you gain the skills needed to tackle modern industry challenges.

In this machine learning and cybersecurity course, students will explore supervised & unsupervised learning, neural networks, and advanced ML techniques while also diving deep into ethical hacking, penetration testing, network security, and digital forensics. The program also covers computational complexity, NLP techniques, and AI-powered text analysis, making it ideal for professionals looking to expand their expertise.

If you're searching for a "machine learning and cybersecurity course near me," this online training offers a flexible, comprehensive curriculum covering advanced ML models, security defense mechanisms, and AI-driven applications. With practical labs, industry-standard tools, and expert-led training, this course prepares you to excel in the rapidly evolving tech landscape.

SYLLABUS

1Machine Learning Fundamentals
  • Understanding learning problems and designing ML models.
  • Supervised vs. Unsupervised Learning with practical applications.
  • Model evaluation techniques, loss functions, and generalization.
2 Advanced Machine Learning Techniques
  • Neural networks, Perceptron, and backpropagation fundamentals.
  • Bayesian learning, Expectation-Maximization, and belief networks.
  • Ensemble methods: Bagging, Boosting, and Random Forest.
3 Cyber Security & Ethical Hacking
  • Cyber threats, security measures, and risk assessment.
  • Penetration testing, vulnerability scanning, and ethical hacking tools.
  • Intrusion detection, firewalls, and encryption techniques.
4 Automata Theory & Computational Models
  • Finite automata, Turing machines, and computational complexity.
  • Regular expressions, context-free grammars, and parsing techniques.
  • NP-Complete problems and the Church-Turing thesis.
5Natural Language Processing (NLP) & AI
  • Fundamentals of NLP, speech processing, and text classification.
  • N-gram models, named entity recognition, and sentiment analysis.
  • Deep learning applications in NLP, machine translation, and chatbots.