Learning

Study materials, certifications, and courses. Repos serve as learning journals and reference implementations.

Certifications

AWS Generative AI
Developer Professional

AWS Generative AI Developer Pro — Exam Prep GitHub →

Comprehensive study guide for the AWS AIP-C01 certification. Notes, Q&A sets, and hands-on Bedrock projects covering all five domains: AI fundamentals, generative AI concepts, AWS AI services, responsible AI, and security. Includes working code samples for each domain.

AWS Bedrock AIP-C01 Certification
Amazon Bedrock Learning GitHub →

Deep dive into Amazon Bedrock and Agent Core courses. Notes, experiments, and projects covering foundation models, knowledge bases, agents, guardrails, and production deployment patterns on AWS. Serves as a personal reference for building LLM applications.

Agents AWS Bedrock Knowledge Bases
Claude Code Learning GitHub →

Notes, projects, and experiments from the Anthropic Claude Code course. Explores agentic workflows, tool use, prompt engineering patterns, and building production-grade applications with the Claude API. Reference implementations for agent-based systems.

Claude API Agents Jupyter
Vector Databases & Semantic Search

Comprehensive learning guides for vector databases, semantic search, and retrieval systems.

OpenSearch

10-module expert curriculum covering lexical search (BM25), semantic vector search (k-NN), and hybrid search with score normalization. Deep focus on production-grade implementation, RAG integration with Bedrock, and OpenSearch Serverless deployment.

View OpenSearch Curriculum →

Pinecone

Coming soon...

Vector Search Hybrid Search OpenSearch RAG
SageMaker Learning GitHub →

5-module expert curriculum on Amazon SageMaker for machine learning engineering and AWS AIP-C01 exam preparation. Covers fine-tuning with LoRA/QLoRA, endpoint deployment patterns, cost optimization, and AI safety & governance. Hands-on implementations for production ML workflows.

Modules

  • Module 1: Architecture, services, core concepts
  • Module 2: Fine-tuning strategies (LoRA, QLoRA, adapters)
  • Module 3: Deployment patterns & optimization
  • Module 4: Security, compliance & governance
  • Module 5: Practice Q&A & exam scenarios
SageMaker Fine-tuning AIP-C01 MLOps
AWS Security GitHub →

Reference implementation and deep-dive notes into AWS security services and patterns. Covers IAM least-privilege design, Security Hub findings, GuardDuty threat detection, and KMS encryption strategies. Practical reference for securing cloud workloads at the architecture level.

Security AWS IAM GuardDuty Security Hub
Algorithms & Data Structures GitHub →

Practice implementations across sorting, searching, graphs, trees, and dynamic programming — collected from LeetCode, HackerRank, GeeksForGeeks, and Codility. Written in Java with complexity annotations on each solution.

Algorithms Java Big-O
Dynamic Programming GitHub →

Focused collection of dynamic programming solutions — knapsack, LCS, edit distance, coin change, matrix chain multiplication, and more. Each solution includes the recurrence relation, memoization and tabulation variants, and complexity analysis.

Dynamic Programming Java
LeetCode Solutions GitHub →

LeetCode problem solutions organized by pattern: sliding window, two pointers, binary search, BFS/DFS, backtracking, and union-find. Consistent structure makes it easy to recognize patterns in new problems.

Algorithms Java LeetCode
Codility Solutions GitHub →

Efficient solutions to Codility algorithm challenges with a focus on time and space complexity. Covers prefix sums, caterpillar method, leader algorithms, and prime/composite sieve patterns.

Algorithms Java Codility
HackerRank Solutions GitHub →

317+ solutions to HackerRank algorithm and data structure challenges. Covers arrays, strings, trees, graphs, greedy algorithms, and SQL. Organized by domain and difficulty.

Algorithms Java SQL