Open to AI / ML internships & collaborations

Teaching machines to solve problems
while humans keep creating new ones.

Aspiring AI Engineer · Machine Learning Enthusiast · AI Project Builder. Learning in public and building real machine learning, deep learning and computer vision projects — focused on shipping intelligent products that work beyond the notebook.

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Current focus
Machine Learning
Supervised, unsupervised & evaluation rigor.
Deep Learning
Neural networks, CNNs & model architectures.
Computer Vision
Image classification, detection & explainability.
AI Applications
Turning models into usable Streamlit & web apps.
Worflow Automations
Social media automations, repetitive tasks automation.
neural.net // forward pass
● live
Currently: Building ML portfolio projects
● Learning in public
About

An aspiring engineer learning AI by building it.

I'm Ayesha — an aspiring AI engineer and machine learning enthusiast. I learn by building real projects in Python with NumPy, Pandas, Scikit-Learn and TensorFlow, then shipping them as Next.js web apps so the work doesn't stay stuck in a notebook.

My focus right now is machine learning, deep learning and computer vision — and getting better every week.

Curious by default

Breaking models apart to understand why they work.

Hands-on practice

Building, training and evaluating models — not just watching tutorials.

Beyond the notebook

Wrapping experiments into web apps people can actually use.

Honest about progress

Showing real work in real time — no inflated metrics, no fake claims.

Stack

The toolkit behind every model.

Artificial Intelligence

  • Machine Learning
  • Deep Learning
  • Computer Vision

Data Science

  • NumPy
  • Pandas
  • Scikit-Learn
  • Data Visualization

Programming

  • Python
  • C++

Frameworks & Tools

  • TensorFlow
  • Streamlit

Development

  • Next.js
  • Git
  • GitHub
Work

Selected projects.

Production-grade AI products spanning medical imaging, predictive maintenance and RAG knowledge agents.

Journey

Current learning journey.

An honest timeline of where I am — not where I want to look like I am.

  1. 2025

    Started AI & Machine Learning Journey

    Self-directed study
    • Deep dive into Python, NumPy, Pandas and the fundamentals of machine learning.
    • Building a daily habit of taking courses and writing code.
  2. 2026

    Built First Machine Learning Projects

    Hands-on practice
    • Trained classical ML models with Scikit-Learn on real public datasets.
    • Wrapped experiments into Streamlit and next.js apps to learn end-to-end delivery.
  3. 2026

    Developed AI Portfolio Projects

    Open source
    • Built deep learning and computer vision projects published on GitHub.
    • Focused on clean code, documentation and reproducible results.
  4. 2026

    Seeking Real-World Experience

    Open to opportunities
    • Looking for AI / ML projects where I can contribute and grow alongside experienced engineers.
GitHub

Live from the terminal.

@Marfooah
Process

My engineering process.

How I take an AI project from a vague question to something usable.

Step 01

Problem Identification

Define the real-world problem before touching a model.

Step 02

Data Collection

Source relevant, representative datasets.

Step 03

Data Cleaning

Handle nulls, outliers, leakage and bias.

Step 04

Model Development

Iterate from baselines to deep learning architectures.

Step 05

Evaluation

Honest metrics, validation splits and error analysis.

Step 06

Deployment

Ship to next.js web so the model is actually usable.

Contact

Let's build the next system.

Open to AI engineering roles, contract work, and collaborations on ambitious ML products.