AI in Robotics & Autonomous Systems

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand deploying and monitoring models
• Understand working with Python libraries such as NumPy, pandas and scikit-learn

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

Description

Explore AI applications in robotics: path planning, sensor fusion, SLAM, and deep learning for autonomous navigation. Build real robotic simulations with ROS2.

What this course covers: AI in Robotics & Autonomous Systems works through deploying and monitoring models, working with Python libraries such as NumPy, pandas and scikit-learn, supervised and unsupervised learning and feature engineering and data preparation.

Skills you will build: preparing data for training, 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. 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 deploying and monitoring models and preparing data for training 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

• M
  Meghna J. ★★★★★ 5.0/5 Sample review

  Well organised. Revisiting the feature engineering and data preparation part a second time really helped it click. The deployment section filled a gap most ML courses skip.

• F
  Farhan Q. ★★★★★ 5.0/5 Sample review

  AI in Robotics & Autonomous Systems covered working with Python libraries such as NumPy, pandas and scikit-learn in more depth than I expected. The pace was right for my level. The hands-on notebooks made backpropagation finally make sense to me.

• R
  Rahul K. ★★★★★ 5.0/5 Sample review

  Clear structure from deploying and monitoring models through to working with Python libraries such as NumPy, pandas and scikit-learn. I'd like a few more practice tasks, but overall very useful. Loved that model evaluation got as much attention as model building.

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