I will build a fast ml prototype notebook and evaluation

A
ahmed_ml22
A
ahmed_ml22
M Ahmed Imtiaz
build a fast ml prototype notebook and evaluation
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build a fast ml prototype notebook and evaluation
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About this gig

I will create a working ML prototype (classification or regression) on your dataset and deliver a reproducible Jupyter notebook, the trained model checkpoint, and an evaluation report. My deliverables include: cleaned data sample, preprocessing steps, baseline model (scikit-learn / PyTorch), evaluation metrics (AUC/F1/RMSE as appropriate) and next-step recommendations.

What you get:

  • Reproducible Jupyter notebook with data cleaning, feature pipeline, and model training.
  • Model checkpoint (pickle / joblib / .pth as appropriate) and a simple prediction script.
  • Evaluation report (confusion matrix, precision/recall/F1 or RMSE/AUC) and short next steps note.
  • I start with a small feasibility run on a sample you provide (50200 rows) so you see results quickly.

  • Model expertise

    • Custom model development
    • Fine-tuning models
    • Generative AI

  • Industry

    • Other

  • Programming language

    • Python

  • Language

    • English
    • Urdu

  • Technical expertise

    • Machine learning (Supervised, Unsupervised, Reinforcement)
    • Deep learning (Neural networks, GANs)
    • Feature engineering and data processing

Get to know M Ahmed Imtiaz

M Ahmed Imtiaz
  • FromPakistan
  • Member sinceSep 2025

  • Languages

    Urdu, English
I’m an ML engineer who builds fast, production-ready prototypes and clean data pipelines. My hands-on experience includes BERT fine-tuning for sentiment and text classification, graph-based recommenders (Node2Vec / GraphSAGE), and wrapping models into FastAPI endpoints with Docker. I focus on reproducible deliverables: Jupyter notebooks, model checkpoints, inference scripts, and clear run instructions so clients can validate and extend work easily. For example, in my DamTechhub internship I improved a GraphSAGE recommender’s validation AUC and produced a working FastAPI inference endpoint.

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