AI in Cybersecurity & Threat Detection

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand feature engineering and data preparation
• Understand model training and evaluation

Planned outline shown above (sample metadata). The detailed module and lesson list for this course hasn't been published yet.

Description

Apply AI to detect and prevent cyber threats. Build anomaly detection, malware classification, and network intrusion detection systems using ML and deep learning.

What this course covers: AI in Cybersecurity & Threat Detection works through feature engineering and data preparation, model training and evaluation, supervised and unsupervised learning and deep learning frameworks (TensorFlow / PyTorch).

Skills you will build: tuning model performance, deploying models to production and building and evaluating ML models.

Practical applications: prediction and classification systems, computer vision and NLP applications and AI features inside products.

Who should take this course: developers, data analysts and engineering students moving into machine learning and AI. It is designed for learners with solid experience in machine learning and AI who want advanced, in-depth practice.

Learning outcomes: by the end of the course you should be able to apply feature engineering and data preparation and tuning model performance to your own work or projects, and know which areas to practise next.

Requirements

• Solid working knowledge of machine learning and AI
• Comfort with intermediate concepts
• A computer with internet access

Your instructor

• V
  Vivek Y. ★★★★★ 4.5/5 Sample review

  Well organised. Revisiting the deep learning frameworks (TensorFlow / PyTorch) part a second time really helped it click. Loved that model evaluation got as much attention as model building.

• R
  Ritika M. ★★★★★ 4.0/5 Sample review

  AI in Cybersecurity & Threat Detection covered model training and evaluation in more depth than I expected. The pace was right for my level. The deployment section filled a gap most ML courses skip.

• M
  Madhuri I. ★★★★★ 4.5/5 Sample review

  I joined to get better at tuning model performance. The practical exercises on feature engineering and data preparation were exactly what I needed. The hands-on notebooks made backpropagation finally make sense to me.

Sample reviews are illustrative demo content, not verified purchases. Ratings from learners will appear here once submitted.