AI for Digital Marketing & Personalization

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand neural network architectures
• Understand supervised and unsupervised learning

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

Description

Leverage AI to supercharge digital marketing. Build recommendation engines, customer segmentation models, churn predictors, and AI-powered ad optimization systems.

What this course covers: AI for Digital Marketing & Personalization works through neural network architectures, supervised and unsupervised learning, deep learning frameworks (TensorFlow / PyTorch) and model training and evaluation.

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

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. No prior experience is needed — the course starts from the basics.

Learning outcomes: by the end of the course you should be able to apply neural network architectures and building and evaluating ML models to your own work or projects, and know which areas to practise next.

Requirements

• No prior experience required
• A computer or smartphone with internet access
• Time to practise each week

Your instructor

• F
  Farhan Q. ★★★★★ 4.5/5 Sample review

  AI for Digital Marketing & Personalization covered deep learning frameworks (TensorFlow / PyTorch) in more depth than I expected. The pace was right for my level. Loved that model evaluation got as much attention as model building.

• N
  Neha A. ★★★★★ 4.0/5 Sample review

  Well organised. Revisiting the deep learning frameworks (TensorFlow / PyTorch) part a second time really helped it click. The deployment section filled a gap most ML courses skip.

• P
  Pooja H. ★★★★★ 4.5/5 Sample review

  Good balance of theory and practice. The neural network architectures lessons helped me apply building and evaluating ML models in real situations. 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.