AI & ML Solutions

End-to-end machine learning pipelines — from raw data to trained, validated, and visualized models ready for use.

Artificial Intelligence5 min read

Machine Learning That Actually Ships

Not just model training — full pipelines. Data preprocessing, architecture selection, training, evaluation, and output visualization, delivered as something usable.

AI & ML Solutions

What This Involves

I've built stacked LSTMs for time-series forecasting, CNNs for image classification, and physics-informed neural networks to solve PDEs — all trained, validated, and benchmarked against real metrics (RMSE, MAE, exact analytical solutions). I know how to build models that aren't just accurate on paper.

I work primarily in Python with PyTorch, scikit-learn, and the standard data science stack (Pandas, NumPy, Matplotlib, Seaborn). I handle everything from raw dataset cleaning and feature engineering through to final output rendering — including 2D, 3D, and animated visualizations.

Data Preprocessing

Cleaning, normalization (MinMaxScaler, StandardScaler), feature engineering, and train/test splitting done right.

Model Training

Architecture selection, hyperparameter tuning, and multi-epoch training with validation curves tracked throughout.

Evaluation & Metrics

Rigorous evaluation with appropriate metrics — RMSE, MAE, accuracy, F1 — compared against baselines.

Visualization Output

Clean, interpretable plots — predicted vs. actual, loss curves, confusion matrices, and animated visualizations.

Typical ML Project Flow

1

Problem Definition & Data Audit

We clarify what success looks like — target variable, evaluation metric, and data availability — before choosing an approach.

Week 1
2

Preprocessing & EDA

Exploratory data analysis, cleaning, normalization, and feature engineering to get data model-ready.

Week 1–2
3

Model Development & Training

Architecture built and trained iteratively — with validation loss tracked and hyperparameters tuned throughout.

Week 2–4
4

Evaluation & Handover

Final benchmarking against metrics, clean visualization output, and documented code ready for handover or integration.

Week 4–5

Service Overview

4k+
Training Epochs
PyTorch
Primary Framework
5wks
Avg. Timeline
LanguagesPython, R
FrameworksPyTorch, scikit-learn
VisualizationMatplotlib, Seaborn, Power BI
Model TypesLSTM, CNN, PINN, SEIR
DeliverableTrained model + documentations

"His ability to bridge theoretical research and practical implementation is rare. The models were rigorous, the code was clean."

Dr. Oyoon Abdul Razzaq
Dr. Oyoon Abdul Razzaq
Research SupervisorComputational Modeling Lab
4000+Epochs Trained
Validatedvs. Exact Solutions

Discuss Your ML Problem

Tell me about your dataset and what you're trying to predict or classify.