This repository showcases project work completed by Harish Lingam as part of various Udacity Nanodegree programs. As a product manager with a growing focus on technical depth, I'm leveraging these projects to deepen my skills in AI, machine learning, and data-informed product development.
Each subfolder contains standalone project work, complete with documentation, data analysis, and strategic deliverables.
This program explores how to integrate AI and machine learning into real-world product management. Key topics include identifying high-impact use cases, collaborating with technical teams, defining success metrics, and designing responsible AI products.
Projects include:
- Framing ML product opportunities with clear success metrics
- Creating AI delivery roadmaps across business and engineering teams
- Identifying risks related to bias, explainability, and misuse of AI
This program focuses on using data science and analytics to shape product direction. Topics include infrastructure strategy, product KPIs, and iteration planning based on quantitative insights.
Projects include:
- Evaluating monetization performance and recommending pricing changes
- Designing a scalable data infrastructure strategy
- Conducting activation and retention cohort analyses
This program focuses on product-led growth strategies, from user acquisition and activation to retention and monetization. Emphasis is placed on experimentation and building scalable growth loops.
Projects include:
- Developing a growth loop and go-to-market plan for a DTC snack brand
- Running activation and retention funnel analyses for a Slack-style app
- Proposing monetization improvements for a B2B SaaS company
This program focuses on building products powered by large language models (LLMs). Projects emphasize prompt engineering, model fine-tuning, and efficient deployment using parameter-efficient techniques.
Projects include:
- Fine-tuning a BERT-based model using LoRA (PEFT) on the
dair-ai/emotiondataset - Training and evaluating emotion classification pipelines using Hugging Face and PyTorch
(Coming soon)
This upcoming track will explore foundational NLP concepts including tokenization, sequence modeling, and applications such as sentiment analysis, summarization, and machine translation.
This portfolio demonstrates my:
- Growth as a technical product manager with strong AI/ML fluency
- Experience collaborating across product, data science, and engineering teams
- Emphasis on experimentation, metrics, and outcome-driven strategy
- Commitment to building responsible, user-centered AI solutions
- Python, Jupyter Notebooks
- Hugging Face Transformers, Datasets, and PEFT
- PyTorch, LoRA adapters
- SQL, Tableau, Excel
- Product analytics (activation, retention, monetization)
- GitHub for version control and project documentation
| Folder Name | Description |
|---|---|
ai-product-manager-nanodegree/ |
PM-focused AI projects, including delivery roadmaps and ethical evaluations |
data-product-manager-nanodegree/ |
Monetization modeling, analytics strategy, and data infrastructure planning |
growth-product-manager-nanodegree/ |
Growth loops, retention funnels, and SaaS monetization improvement |
generative-ai-nanodegree/ |
LLM fine-tuning, prompt engineering, and model deployment projects |
natural-language-processing/ |
(Coming soon) Foundational NLP projects and modeling workflows |
Feel free to explore, fork, or reach out if you'd like to connect on product, AI, or data strategy.