Machine learning & deep learning research, from classical algorithms to large language models.
A current view of my research workflow across 20 full projects: from data analysis and model building to deployment, databases, and MLOps.
I approach machine learning as a full research-to-production pipeline. I start with Exploratory Data Analysis (EDA), data cleaning, preprocessing, feature engineering, and visualization to understand the real structure of the data before choosing a model.
From there, I build a strong baseline first, then improve it through cross-validation, metric tracking, error analysis, threshold calibration, and controlled experiments. I care about what each model learns, where it fails, and whether the final result is actually useful beyond a notebook score.
My current project archive now covers medical AI, computer vision, NLP, tabular machine learning, regression, classification, object detection, segmentation, web scraping, and data engineering. The newer projects go beyond notebooks into deployed systems with APIs, databases, Docker, model logging, and experiment tracking.
For deep learning projects, I focus on practical architecture decisions: CNN backbones for image classification, YOLO-style detection for localization, U-Net-style segmentation for medical masks, and sequence models such as BiLSTM, attention, Siamese networks, embeddings, TF-IDF, and NLP preprocessing for text tasks.
For classical machine learning, I still treat algorithms as serious production tools. I compare linear models, SVMs, tree models, Random Forests, XGBoost, LightGBM, CatBoost, voting ensembles, feature engineering strategies, and tabular validation workflows before deciding whether deep learning is really necessary.
The direction is moving from isolated experiments toward complete AI systems: Lung Disease Detection, Quora Question Pairs, Disaster Tweets, SMS Spam, Global Wheat Detection, Cassava Leaf Diseases, TMDB Revenue, and earlier classical ML/data-analysis projects all connect model performance with real implementation practice.
On the deployment side, I connect trained models with real interfaces using FastAPI, Docker, SQL databases, object storage, ONNX export, inference pipelines, prediction logging, and MLOps tools such as MLflow, Airflow, Kafka, and Optuna. The goal is not only to train models, but to understand how they become reliable AI products.
A visual project vitrine where image scale represents project priority.
View all projects →Deep technical skills developed through research and competition.
Amir Mohammad Askari — AI engineer and researcher.