My Summer Internship

This summer wasn’t about beaches or binge‑watching — it was about stepping into the world of Artificial Intelligence and Machine Learning. I enrolled in the IBM AI Engineering Professional Certificate, and my days quickly turned into Python notebooks, late‑night debugging, and the joy of models working as expected.

🏗️ Building Blocks of My Journey

• Machine Learning: regression, classification, clustering, Decision Trees, Random Forest, KNN
• Deep Learning: CNNs, RNNs/LSTMs, Transformers, TensorFlow, PyTorch, Keras
• Generative AI: GPT, BERT, fine‑tuning (LoRA, QLoRA), LangChain + vector DB

🤖 Highlight Project: Knowledge‑Aware Bot

Built an AI agent that reads documents and answers questions. Tech used: LangChain, Vector DB, Gradio. It wasn’t flawless (sometimes hallucinated) but proved the power of Retrieval + LLMs.

🌟 Skills I Gained

• Programming: Python, scikit‑learn, TensorFlow, PyTorch
• AI Concepts: Neural nets, embeddings, Transformers, RAG
• Data Work: preprocessing structured + unstructured data
• Apps: LangChain projects, Gradio UIs, API integration

🎭 Challenges Faced and Lessons Learned

• Models are often less about finding a single answer and more about managing trade-offs.
• The most complex part of a project is usually a data problem, not a code problem.
• You spend 90% of your time on the 10% of the code that doesn't work.
• Sometimes, a simple linear model works better than a fancy neural network.
• A model is only as good as the question you teach it to answer.

🌍 Beyond the Code

This journey showed me how AI is reshaping industries — from smarter healthcare to creative AI copilots. It also taught me that successful AI requires responsibility, empathy, and imagination, not just algorithms.

📝 Closing Thoughts

I began curious and ended confident — able to build, fine‑tune, and deploy real AI applications. This summer wasn’t just an internship; it was the foundation of my journey in AI & ML.

AI Machine Learning Deep Learning LangChain RAG

📜 Certificate