Notebook for quick search

Slides and notebooks for my tutorial at PyData London 2018

Notebooks explaining various Machine Learning concepts.

Python Notebook for Ensemble Learning on Wisconsin Breast Cancer Database

AIND Jupyter Notebook to predict student admissions using Keras Neural Networks

this notebook predicts the prices of California housing using Stochastic Gradient and SVM classifier.

📶 Logistic regression classifier for bit decoding in binary vectors using stochastic gradient descent (SGD). Features performance evaluation, probabilistic modeling, confusion matrix analysis, and classification error interpretation. Developed in Python with Jupyter Notebook.

I build the Micrograd autogradient engine, which is a functioning neural network with forward pass, backward propagation, and stochastic gradient descent, all built from scratch. This is derived from the great @karpathy micrograd lecture. Each notebook is complete with Andrei's lecture code and speech, as well as my own code, anecdotes and addition

Portfolio of Jupyter Notebooks demonstrating various ML models/concepts learned and developed during my graduate machine learning course and independently post-grad. Generally, a bottom-up modeling approach with Numpy is used to showcase grasp of mathematical foundation. Higher-level libraries (scikit-learn) used for optimized algo implementations.

Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.