Applied AI Specializations — Skills Plan
Planned skill map for Applied AI Specializations. The curriculum will be developed and released in stages.
Compare computer vision, NLP, speech, recommendation, and multimodal pathways, focusing on dataset design, task-specific evaluation, and deployment constraints.
Potential roles: Computer Vision Engineer · NLP Engineer · Speech Engineer · Recommendation Systems Engineer
Before you start
- Python, data handling, and software testing
- Linear algebra, probability, and statistics
- Machine-learning foundations and evaluation
What you’ll build
- Build an evaluated vision or speech application
- Develop a recommendation baseline and ranking analysis
- Compare a multimodal model against a task-specific baseline
Prerequisites
PLANNED- Python, data handling, and software testing
Build the Python, data handling, and software testing knowledge needed to follow the Applied AI Specializations foundations.
- Linear algebra, probability, and statistics
Build the Linear algebra, probability, and statistics knowledge needed to follow the Applied AI Specializations foundations.
- Machine-learning foundations and evaluation
Build the Machine-learning foundations and evaluation knowledge needed to follow the Applied AI Specializations foundations.
Foundations
PLANNED- Dataset design, labeling, and leakage prevention
Explain how Dataset design, labeling, and leakage prevention works and where it fits in the Applied AI Specializations learning path.
- Neural-network training and optimization
Explain how Neural-network training and optimization works and where it fits in the Applied AI Specializations learning path.
- Experiment tracking and reproducibility
Explain how Experiment tracking and reproducibility works and where it fits in the Applied AI Specializations learning path.
- Responsible AI and model limitations
Explain how Responsible AI and model limitations works and where it fits in the Applied AI Specializations learning path.
Core Skills
PLANNED- Computer vision: image classification and detection
Use Computer vision: image classification and detection to implement a working capability and verify its behavior in Applied AI Specializations.
- NLP: text representation and task modeling
Use NLP: text representation and task modeling to implement a working capability and verify its behavior in Applied AI Specializations.
- Speech and audio: recognition and processing
Use Speech and audio: recognition and processing to implement a working capability and verify its behavior in Applied AI Specializations.
- Recommendation and ranking systems
Use Recommendation and ranking systems to implement a working capability and verify its behavior in Applied AI Specializations.
- Multimodal model concepts
Use Multimodal model concepts to implement a working capability and verify its behavior in Applied AI Specializations.
Advanced / Production
PLANNED- Domain-specific evaluation and error analysis
Evaluate Domain-specific evaluation and error analysis trade-offs against the performance, reliability, and safety needs of Applied AI Specializations systems.
- Inference optimization and model serving
Evaluate Inference optimization and model serving trade-offs against the performance, reliability, and safety needs of Applied AI Specializations systems.
- Data drift and retraining controls
Evaluate Data drift and retraining controls trade-offs against the performance, reliability, and safety needs of Applied AI Specializations systems.
- Edge AI and resource constraints
Evaluate Edge AI and resource constraints trade-offs against the performance, reliability, and safety needs of Applied AI Specializations systems.
- Reinforcement-learning foundations and safe evaluation
Evaluate Reinforcement-learning foundations and safe evaluation trade-offs against the performance, reliability, and safety needs of Applied AI Specializations systems.
Real-World Projects
PLANNED- Build an evaluated vision or speech application
Deliver a working outcome with Build an evaluated vision or speech application, then test it against the project requirements.
- Develop a recommendation baseline and ranking analysis
Deliver a working outcome with Develop a recommendation baseline and ranking analysis, then test it against the project requirements.
- Compare a multimodal model against a task-specific baseline
Deliver a working outcome with Compare a multimodal model against a task-specific baseline, then test it against the project requirements.
Career / Interview Preparation
PLANNED- Specialization-specific model design
Explain your approach to Specialization-specific model design, defend key trade-offs, and discuss what you learned.
- Dataset and evaluation review
Explain your approach to Dataset and evaluation review, defend key trade-offs, and discuss what you learned.
- Responsible deployment walkthrough
Explain your approach to Responsible deployment walkthrough, defend key trade-offs, and discuss what you learned.
Discussion
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