From QA to Machine Learning Engineer in 90 Days: Shreya’s Journey
Meet Shreya Gupta, a QA analyst who leveraged our Machine Learning & AI Fundamentals program to transition into a Machine Learning Engineer role at a leading fintech startup—in just three months.
Background & Motivation
- 2 years as a QA analyst, eager for a deeper technical challenge
- Inspired by AI breakthroughs but intimidated by advanced math
- Committed to upskilling alongside her full-time job
Tools & Technologies Used
Programming & Frameworks
- Python 3.8+
- scikit-learn • TensorFlow • PyTorch
- Flask & Streamlit for deployment
Data & Visualization
- Pandas & NumPy for data handling
- Matplotlib & Seaborn for plotting
- Jupyter Notebooks for interactive experiments
Week-by-Week Progress
- Weeks 1–2: Python refresher & linear regression—built a housing price predictor notebook.
- Weeks 3–4: Classification models—implemented spam detection & visualized confusion matrices.
- Weeks 5–6: Deep learning basics—trained a CNN on MNIST with 92 %+ accuracy.
- Weeks 7–8: Deployment & prep—deployed a Flask API on Heroku; completed mock interviews.
Challenges & How She Overcame Them
- Complex Concepts: Broke them into daily 30-minute sessions.
- Debugging: Leveraged Slack community and weekly mentor office hours.
- Time Management: Followed a strict 90 min/day schedule with weekend catch-ups.
Key Wins & Outcomes
- Built 6 portfolio-ready ML projects
- Secured 2 internship offers within a month
- Achieved a 50 % salary bump (₹4 LPA → ₹6 LPA)
- Hired as Junior ML Engineer at a top fintech firm
“Weekly mentor reviews guided me past every roadblock—I couldn’t have done it alone.”
Shreya’s Advice to Future Students
- Code Daily: Consistency over cramming—1 hr/day works wonders.
- Ask Questions: Use Slack and mentor calls—no question is too small.
- Build Projects: Hands-on tasks solidify learning far better than lectures.
Ready to Write Your Own Success Story?
Join Shreya and dozens of others who launched their ML careers in just 6 weeks.