AI Engineering with Python & Cloud

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 deep learning frameworks (TensorFlow / PyTorch)

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

Description

Build production-grade AI systems using Python, Docker, and cloud platforms. Covers MLOps, model monitoring, CI/CD pipelines for AI, and scalable inference.

What this course covers: AI Engineering with Python & Cloud works through feature engineering and data preparation, deep learning frameworks (TensorFlow / PyTorch), transformers and large language models and neural network architectures.

Skills you will build: tuning model performance, building and evaluating ML models 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. It suits learners who already know the basics of machine learning and AI and want to go deeper.

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

• Basic knowledge of machine learning and AI
• A computer with internet access
• Willingness to complete practice tasks

Your instructor

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

  Clear structure from transformers and large language models through to neural network architectures. I'd like a few more practice tasks, but overall very useful. The hands-on notebooks made backpropagation finally make sense to me.

• R
  Rohan P. ★★★★★ 4.5/5 Sample review

  The section on deep learning frameworks (TensorFlow / PyTorch) was explained step by step, and the examples made transformers and large language models much easier to follow. Loved that model evaluation got as much attention as model building.

• A
  Ananya B. ★★★★★ 4.0/5 Sample review

  Good balance of theory and practice. The feature engineering and data preparation lessons helped me apply tuning model performance in real situations. The deployment section filled a gap most ML courses skip.

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