Few Machine Learning and AI themed projects using Python 3.7 and its libraries, such as:
- Matplotlib
- Sklearn
- Numpy
- Tensorflow
- OpenCV
- Seaborn
- Pyttsx
- Gym
- YOLO
- IBM
- Pytorch
- Pygame
All of the projects here are based on online tutorials or official documentation.
Simple implementation and usage of the ChatGPT model. Using gradio to create your own chatbot.
Basic chatbot, that you can use on your website.
A bunch of notes on Deep Learning, AI and Machine Learning.
Detecting various items on images (includes exoplanet hunting project, as well as commentary and implementation of hand gesture recognizer).
Generative adversarial network (GAN) projects.
Various useful notes. Helpful when getting ready for Machine Learning interviews.
Various projects dedicated for exploring sklearn library and popular Machine Learning algorithms such as K Nearest Neighbours (KNN).
Trying out various Machine Learning libraries (Altair, Bokeh, Datapane, etc).
Mammography project. Detecting breast cancer with Neural Networks.
Notes to prepare for ML or AI interview (Andrew Ng).
Experimenting with filters, haar cascades, timelapses. Video and photo features.
A* Pathfinding algorithm.
Using YOLO library to main points on human body.
Few projects just to get familiar with PyTorch library.
Quantitative programming using IBM Python supporting library.
Few projects just to get familiar with R language, syntax, possibilities and R Studio.
Reinforcement Learning projects tested with gym library and its environments.
Based on the tutorial from Andrew NG about building neural networks from the scratch.
Computer Vision and CARLA projects.
Few projects just to get familiar with the Sklearn library.
Snake game made with PyGame. Implemented ML algorithms to help the app learn the game rules and high scores.
Projects related mostly with voice recognition; e.g. a voice assistant similiar to f. e. Alexa, converting PDF to Audiobook format with pyttsx3 library
Sudoku game made with PyGame. Implemented self-solving recursive algorithm.
Document summarization with Sklearn and Deep Learning.
Various resources - notes and exercises about Machine Learning, Neural Networks and AI.
Exploring Seaborn, Matplotlib's plots and Panda's plots. Lots of data vizualisations (who would have guessed).