This repository contains a series of Jupyter Notebooks I've worked on throughout my studies in Artificial Intelligence, Machine Learning, and Data Science.
- Univariate Linear Regression Implementation with Batch Gradient Descent from Scratch
- Bivariate Linear Regression Implementation with Mini-Batch Gradient Descent from Scratch
- Logistic Regression Implementation from Scratch
- Logistic Regression Implementation Using TensorFlow and Keras
- Decision Tree Classification Implementation Using Scikit-Learn
- Random Forest Classification Implementation Using Scikit-Learn
- Naïve Bayes Classification Implementation Using Scikit-Learn
- k-Nearest Neighbors Classification Implementation from Scratch
- Support Vector Machine (SVM) Implementation Using Scikit-Learn
- Implementing 2-D Convolution from Scratch
- Implementing 2-D Max-Pooling from Scratch
- Visualizing MNIST Digit Averages
- Fine-Tuning DenseNet121 for Image Classification
- MNIST Digit Classification with a Fully-Connected Neural Network
- MNIST Digit Classification with a Convolutional Neural Network (CNN)
- MNIST Fashion Item Classification with a Fully-Connected Neural Network
- MNIST Fashion Item Classification with a Convolutional Neural Network (CNN)