Edge AI & TinyML for IoT Devices

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 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

Deploy machine learning on edge devices and microcontrollers. Build real-time AI applications using TensorFlow Lite, ONNX, and hardware accelerators for IoT.

What this course covers: Edge AI & TinyML for IoT Devices works through feature engineering and data preparation, supervised and unsupervised learning, working with Python libraries such as NumPy, pandas and scikit-learn and model training and evaluation.

Skills you will build: preparing data for training, 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 preparing data for training 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

• D
  Diya R. ★★★★★ 4.5/5 Sample review

  I joined to get better at deploying models to production. The practical exercises on working with Python libraries such as NumPy, pandas and scikit-learn were exactly what I needed. Loved that model evaluation got as much attention as model building.

• L
  Lavanya P. ★★★★★ 5.0/5 Sample review

  Good balance of theory and practice. The supervised and unsupervised learning lessons helped me apply building and evaluating ML models in real situations. The deployment section filled a gap most ML courses skip.

• P
  Pranav D. ★★★★★ 4.0/5 Sample review

  Clear structure from feature engineering and data preparation through to supervised and unsupervised learning. I'd like a few more practice tasks, but overall very useful. 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.