Ridge · Lasso · ElasticNet — From Scratch
Regularized linear models written line-by-line — the core math implemented by hand.
github.com/avilaqba/Ridge-Lasso-ElasticNet-Regression-From-Scratch →// open source · github.com/avilaqba
Open Source — Proof by Construction
23 public repositories. One discipline: I build machine learning from scratch so I can see inside every model — and then I share the code. The public arc runs from-scratch → federated → applied & scientific.
// the record
One account, one through-line.
Everything on github.com/avilaqba is built from the inside out. I don't just call sklearn and move on — I write the math, the gradient, the optimization loop, the decision boundary, by hand, so that when I later build on top of these primitives (federated training, applied forecasting, connectomics), I trust what's under the hood. The 23 repos below are the public trail of that habit, in three phases.
PHASE 01
From Scratch
The foundations. Regularized linear models, optimization loops, and a probabilistic classifier — all written from first principles, no off-the-shelf estimation.
Gradient Descent vs SGD — From Scratch
The optimizer battle for linear regression, implemented by hand to show the bias/variance computation trade-off.
github.com/avilaqba/Gradient-Descent-VS-Stochastic-Gradient-Descent-From-Scratch-For-Linear-Regression →Binary Bayes Classifier — From Scratch
A probabilistic classifier built from the fundamentals of Bayesian inference.
github.com/avilaqba/BinaryBayesClassifier_FromScratch →
PHASE 02
Distributed & Applied
From single models to coordination across data silos — then the applied forecasting and predictive modeling that mirrors my commercial data-science work.
Federated Learning with Logistic Regression
Private, distributed training across data silos without moving the data — the on-ramp to distributed learning.
github.com/avilaqba/Federated-Leraning-with-Logistic-Regression-Classifier →Bank Customer Churn Prediction
Applied predictive modeling on a real business problem — the notebook-to-service mindset.
github.com/avilaqba/bank_customer_churn_prediction →Forecasting — Bus Demand · Solar Power · Incoming Calls
Time-series prediction across three domains: bus demand, solar-power generation, and incoming-call volume.
explore the forecasting notebooks on github.com/avilaqba →
PHASE 03
Scientific
Where the thread reaches biology and emergent computation — connectomics, molecular-graph theory, and population-based search.
Brain Connectivity Matrix Super-Resolution
Recovering high-resolution brain wiring graphs from coarse connectomes — released as a CC0 Kaggle dataset (289 downloads).
github.com/avilaqba/brain_connevtivity_supper_reolution →Morgan Algorithm — Unique Atom Numbering
Graph-theoretic canonical numbering of atoms in molecules — chemistry + graph theory + code.
github.com/avilaqba/Morgan_algorithm →TSP with a Genetic Algorithm
Combinatorial optimization via evolutionary search — population-based, emergent problem solving before I did swarms.
github.com/avilaqba/TSP_GA →An honest note on the LLM & agentic work.
My recent work — LLM applications and agentic AI systems, orchestration, tool use, memory, and evaluation — is professional work done at Property Turkey, and it is not yet open-sourced on this account. This page is a deliberate record of what is public: a trajectory that starts with building ML from scratch, moves through federated/distributed learning, and lands in applied and scientific work. The public code shows the foundation the agent work stands on — it doesn't overclaim the agent work itself as open source.