Applied AI Career Transition Program

• NumPy for numerical computing and vectorization
• Pandas for structured data manipulation
• Data cleaning (missing values, outliers, transformations)
• Exploratory Data Analysis (EDA) techniques
• Visualization using Matplotlib & Seaborn
• Building reusable data pipelines

• Writing scalable and modular Python code
• APIs, JSON handling, and system integration
• Working with external libraries and environments
• Structuring real-world AI projects
• Logging, configuration, and debugging workflows
• Backend logic fundamentals for AI applications

• Regression and classification models
• Decision trees, ensemble methods, and SVM
• Clustering and dimensionality reduction
• Bias, variance, and overfitting concepts
• Model evaluation (accuracy, precision, recall, F1)
• Understanding ML pipelines and workflows

• Neural networks and activation functions
• Backpropagation and gradient optimization
• Training workflows (loss functions, optimizers)
• CNN and sequence models (conceptual)
• Regularization techniques (dropout, batch norm)
• Debugging and improving model performance

• Attention mechanism and transformer architecture
• Tokenization methods (BPE, WordPiece)
• Context windows and sequence modeling
• Next-token prediction in LLMs
• Using Hugging Face models and pipelines
• Fine-tuning vs pre-trained model usage

• Prompt engineering techniques (zero-shot, few-shot, CoT)
• Structured prompting and response control
• System prompts and role-based prompting
• Function calling and JSON outputs
• Controlling LLM behavior and consistency
• Debugging and optimizing prompts

• Working with OpenAI, Claude, Gemini APIs
• Building AI chatbots and assistants
• Managing conversation flow and context
• Integrating LLMs into backend systems
• Handling API limits and optimization
• Building production-ready AI features

• Generating embeddings using transformer models
• Understanding vector representation of text
• Cosine similarity and vector matching
• Building semantic search systems
• Optimizing embedding quality
• Handling large-scale text data

• Working with FAISS and ChromaDB
• Indexing embeddings for fast retrieval
• Designing retrieval pipelines
• Optimizing search latency
• Managing updates and persistence
• Real-world use cases of vector DBs

• Designing RAG pipelines
• Data ingestion and chunking strategies
• Context injection into LLMs
• Building document-based AI systems
• Handling multi-source data
• Improving response relevance

• Multi-query and hybrid retrieval
• Reranking techniques
• Reducing hallucinations
• Context optimization strategies
• Combining structured and unstructured data
• Building enterprise-grade RAG systems

• Building workflows using LangChain
• Chains, retrievers, and integrations
• Connecting LLMs with databases and APIs
• Designing reusable AI pipelines
• Managing prompts and responses
• Real-world orchestration of applications

• Understanding AI agents and workflows
• Tool usage and API integrations
• Planning and reasoning systems
• Memory handling (short & long term)
• Autonomous decision-making workflows
• Building intelligent assistants

• State-based workflows using LangGraph
• Designing multi-agent systems
• Task delegation and coordination
• Agent communication patterns
• Building collaborative AI systems
• Managing complex workflows

• Understanding MCP architecture
• Connecting models, tools, and workflows
• Designing modular AI systems
• Managing context across components
• Building scalable AI architectures
• Real-world MCP implementations

• LLM evaluation techniques
• Hallucination detection and mitigation
• Output validation and structured responses
• Safety filters and guardrails
• Improving reliability of AI systems
• Designing trustworthy AI systems

• Prompt versioning and experiment tracking
• Monitoring latency, token usage, and cost
• Logging and tracing LLM interactions
• Tools: LangSmith, W&B, Helicone
• Debugging and improving response quality
• Building observability pipelines

• Deploying and scaling AI systems onAWS 
• Scaling systems using caching and batching
• Failure handling and retry strategies
• Performance optimization
• Deployment using FastAPI and Docker
• Production AI system design
• Handling real-world traffic

• Azure AI services overview
• Deploying AI applications on cloud
• Managing compute and storage
• Scaling AI systems on cloud
• Cost optimization strategies

Preparing for Azure AI-901 certification

• CV Preperation
• Interview Preperation
• LinkedIn Profile Update
• Expert Tips & Tricks