The CPMAI Machine Learning Framework helps teams master ML skills from fundamentals to advanced applications:
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