Quantum Computing for AI & ML

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

Explore the intersection of quantum computing and AI. Learn quantum circuits, variational algorithms, and hybrid quantum-classical ML using Qiskit and PennyLane.

What this course covers: Quantum Computing for AI & ML works through feature engineering and data preparation, model training and evaluation, transformers and large language models and deep learning frameworks (TensorFlow / PyTorch).

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

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 building and evaluating ML models 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
  Vihaan T. ★★★★★ 5.0/5 Sample review

  I joined to get better at tuning model performance. The practical exercises on transformers and large language models were exactly what I needed. Loved that model evaluation got as much attention as model building.

• N
  Neha A. ★★★★★ 5.0/5 Sample review

  Well organised. Revisiting the transformers and large language models part a second time really helped it click. The deployment section filled a gap most ML courses skip.

• N
  Nikhil E. ★★★★★ 5.0/5 Sample review

  Good balance of theory and practice. The feature engineering and data preparation 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.