Domain 3. Machine Learning

Task 1: Applying Classification and Clustering Algorithms

  • Implement appropriate classification algorithms for specific business problems
  • Apply ensemble methods to improve model performance
  • Implement clustering algorithms to discover patterns in unlabeled data
  • Design reinforcement learning approaches with appropriate agents and environments
  • Balance exploration versus exploitation in reinforcement learning systems
  • Evaluate algorithm performance against business requirements

Task 2: Implementing Neural Networks and Deep Learning

  • Construct artificial neural networks with appropriate nodes and layers
  • Explain why neural networks provide superior function approximation
  • Apply neural networks across multiple machine learning (ML) problems
  • Design deep learning architectures with appropriate hidden layers
  • Differentiate between various deep learning architectures
  • Evaluate appropriate neural network types for specific business problems

Task 3: Leveraging Generative AI and Large Language Models (LLMs)

  • Determine appropriate applications for generative AI technologies
  • Identify limitations and challenges of generative AI approaches
  • Explain the fundamental operation of LLMs to stakeholders
  • Develop effective prompt engineering techniques
  • Implement fine-tuning of LLMs for specialized domains
  • Design augmented intelligence solutions using generative AI approaches

Task 4: Selecting Machine Learning Tools and Platforms

  • Navigate the AI project training phase effectively
  • Implement techniques to accelerate model training
  • Navigate the fragmented ML platform ecosystem
  • Create cohesive development environments for ML projects
  • Assess ML platform capabilities against project requirements

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