Reinforcement Learning: Theory to Practice

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 deploying and monitoring models

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

Description

Master reinforcement learning algorithms from Q-learning to PPO. Build intelligent agents that learn to play games and solve complex optimization problems.

What this course covers: Reinforcement Learning: Theory to Practice works through neural network architectures, deploying and monitoring models, transformers and large language models and supervised and unsupervised learning.

Skills you will build: deploying models to production, preparing data for training 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 neural network architectures and deploying models to production 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

• R
  Ritika M. ★★★★★ 4.5/5 Sample review

  Clear structure from transformers and large language models through to supervised and unsupervised learning. I'd like a few more practice tasks, but overall very useful. Loved that model evaluation got as much attention as model building.

• S
  Simran Z. ★★★★★ 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.

• I
  Ira M. ★★★★★ 5.0/5 Sample review

  Reinforcement Learning: Theory to Practice covered neural network architectures in more depth than I expected. The pace was right for my level. 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.