About Me
I build machine learning systems that transform raw data into actionable predictions
View ResumeThe Beginning
My journey into machine learning started with a simple question: "Can data actually predict what happens next?"That curiosity led me from writing my first Python script to building end-to-end ML pipelines.
Currently pursuing B.Tech in Computer Science (Data Science) at National Institute of Science and Technology, I've complemented my formal education with hands-on projects and professional certifications from the University of Michigan and Coursera.
What started as curiosity has become a passion for turning messy real-world data into actionable insights that solve real problems.
How I Think Now
After building several ML systems, my engineering philosophy has crystallized around a few core beliefs:
A simple model on clean data outperforms a complex model on messy data.
The best predictions come from understanding why something happens.
If you can't reproduce your results, you don't understand them.
Education
B.Tech Computer Science & Engineering
Data Science
National Institute of Science and Technology
August 2024 – August 2028
Higher Secondary (XII Science)
CBSE
St. Xavier's High School
April 2020 – May 2022
Professional Courses
Applied Machine Learning in Python
University of Michigan • Coursera
April 2024 – June 2024
- •Supervised & unsupervised learning algorithms
- •Model training, testing, and evaluation
- •Feature engineering & data preprocessing
Machine Learning with Python
Coursera • Coursera
January 2024 – March 2024
- •Supervised & unsupervised learning algorithms
- •Model training, testing, and evaluation
- •Feature engineering & data preprocessing
Values
Intellectual Honesty
I say "I don't know" when I don't. I share failures alongside successes.
Continuous Learning
ML evolves fast. I dedicate time weekly to learning new techniques.
Clear Communication
Complex ideas should be explained simply. Jargon hides weak thinking.
Ship & Iterate
Perfect is the enemy of good. Working solutions beat theoretical perfection.
Languages
Working Style
- ✓I prefer async communication for deep work, with sync catchups for alignment
- ✓I document as I build — code comments, READMEs, and decision logs
- ✓I ask questions early rather than making assumptions
- ✓I value honest feedback and give it respectfully in return
Environments I Avoid
Being upfront about what doesn't work for me:
- ✕Teams where metrics don't matter (decisions by opinion, not evidence)
- ✕Environments that punish honest mistake acknowledgment
- ✕Pressure to deploy models without proper validation
Skills Overview
What People Say
Feedback from people I've worked with and learned from
"Kumlesh demonstrated exceptional problem-solving skills during our ML training program. His ability to break down complex problems into manageable components is impressive."
Training Mentor
Senior Data Scientist at Analytics & ML Training Program
"A dedicated learner who consistently delivers quality work. His churn prediction project showed real understanding of both the technical and business aspects of ML."
Project Supervisor
ML Team Lead at AI Solutions Lab
"Great attention to detail in data preprocessing and feature engineering. Always eager to learn and improve. Would recommend for any ML/data science role."
Prof. Data Science
Associate Professor at NIST