• 🎓 3rd-year Data Science student with a strong interest in applied ML and real-world systems
• 🤖 Exploring Machine Learning, Soft Computing, and Generative AI
• 🧠 Hands-on with Fuzzy Logic, Neural Networks, and intelligent decision systems
• 🧩 Enjoy debugging complex issues across ML pipelines, libraries, and environments
• ⚙️ Curious about how models move from notebooks → production (MLOps / LLMOps)
• 🏎️ Performance & systems nerd (from ML latency to F1 race strategy)
• 📺 Still convinced Dark would’ve been the undisputed GOAT if it were originally in English 😉

Languages	ML / Data	GenAI & LLMs	Tools & Platforms	Concepts
		LLMs, Prompt Engineering, LangChain, Output Parsers		Feature Engineering, PowerTransformer, Cross-Validation, ML Pipelines, Model Evaluation, Data Preprocessing

• 🧠 Machine Learning: Regression, Classification, Feature Selection, Scaling & Transformation

• 🔁 ML Pipelines: End-to-end workflows (data → preprocessing → training → evaluation)

• 📦 MLOps Basics: Model lifecycle, versioning concepts, reproducibility, Dockerized environments

• 💬 LLMs & GenAI:

  • Prompt engineering & structured outputs
  • LangChain chains, templates, and output parsers
  • Understanding latency, tokens, and inference trade-offs

• 🧩 Debugging: Environment issues, dependency conflicts, API & library errors

• 🔁 Bridging ML + MLOps: taking models beyond notebooks
• 🧠 Applying Soft Computing ideas to Industry 5.0 use-cases
• 💬 Building LLM-powered applications using LangChain
• 🔦 Intelligent systems for smart cities, traffic optimization, and automation
• 🛠️ Low-level troubleshooting: BIOS quirks, performance bottlenecks & battery drain analysis

• 🚦 Smart Traffic Controller using Neural Networks + Fuzzy Logic
• 🧠 Fuzzy Logic Smart Lighting System for energy-efficient spaces
• 📈 Real-World ML Regression Pipeline (PowerTransformer + model evaluation)
• 🤝 Friend Recommendation System using Graph Algorithms (BFS / DFS)
• 💬 LLM-based Document Summarizer / Q&A System (LangChain + prompts)
• 📦 Dockerized ML Project showcasing reproducibility & clean environments

• 📦 Docker for ML & GenAI workflows
• ⚙️ MLOps vs LLMOps (deployment, monitoring & scaling concepts)
• 🌐 Frontend basics (HTML, CSS, JavaScript) for ML app interfaces
• 🇩🇪 German language fundamentals (long-term academic & career goals)

“Programming isn't about what you know; it's about what you can figure out.” — Saif Kazi (probably… 😉)

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