This repository contains code and data files for following along with the Machine Learning with Python course. The project is organized into several directories, each focusing on different aspects of machine learning using Python.
The following folders contain projects that were made in order to gain the certification:
- Book Recommendation knn: Contains a project that uses k-nearest neighbors (KNN) to recommend books based on user preferences.
- Cat & Dog: Contains a project that classifies images of cats and dogs using a convolutional neural network (CNN).
- Health Costs Calculator: Contains a project that predicts health insurance costs based on various factors using linear regression.
- RPS: Contains a project that implements a Rock Paper Scissors game using machine learning to predict the player's next move.
- SMS Text Classifier: Contains a project that classifies SMS messages as spam or ham using natural language processing (NLP) techniques.
For each project above, run the main script in the respective folder to execute the project.
The following folders contain scripts demonstrating various machine learning algorithms:
- Classification: Contains scripts for different classification algorithms.
- Clustering: Contains scripts for different clustering algorithms.
- Linear Regression: Contains scripts for linear regression.
The following folders contain scripts demonstrating various neural network architectures:
- Convolutional NN: Contains scripts for convolutional neural networks, including data augmentation and object classification.
- NLP: Contains scripts for natural language processing tasks, such as movie reviews sentiment analysis and RNN-based text generation.
The following folder contains scripts demonstrating Q-learning algorithms:
- frozenlake.py: Contains a script for solving the Frozen Lake environment using Q-learning.
The following folder contains training checkpoints for various models:
- training_checkpoints: Contains checkpoint files for saving and restoring model states during training.