There are many great open source machine learning tools available! Some popular ones include:

1. Scikit-learn - a Python library for machine learning tasks such as classification, clustering, regression, etc. 2) TensorFlow - a JavaScript library used for deep learning tasks such as image recognition, natural language processing, etc.3) Keras - another library that runs on top of TensorFlow to simplify the creation and training of neural networks.4) PyTorch - a competitor to TensorFlow developed by Facebook, known for ease of use and flexibility5) Caffe - an open source framework optimized for speed, used widely across several industries including research.6) MXNet - a scalable deep learning library that supports both CPUs and GPUs.7) Hugging Face - an open source platform for building conversational bots and other AI applications.8) DeepMind Lab - a Unity-based AI lab tool for testing reinforcement learning algorithms.9) OpenCV (Open Source Computer Vision Library) - an open source computer vision library that has been around since 2000.And this list is not exhaustive! There are plenty more options out there depending on your specific needs and interests.

Sure, here are some additional open source machine learning projects you may find interesting:

Blob detection using Deep Learning with OpenCV, YOLOv4 - An implementation of Blob detection using the most recent version of You Only Look Once (YOLO). This project demonstrates how blob detection can be done quickly and accurately using object detection algorithms like YOLOv4.
AWS SageMaker Notebook Instances - SageMaker provides fully managed Jupyter notebook instances running popular development environments so data scientists and developers can build and train ML models quickly and at scale.
Image Processing with Convolutional Neural Networks using Tensorflow - This repository contains code for training a convolutional neural network from scratch which can detect objects within images.
PySpark MLlib tutorial for beginners - This tutorial shows how to write PySpark programs to parallelize over big data, and use Spark’s Machine Learning APIs. It assumes no prior experience in programming, but requires familiarity with command line interfaces and text editors. Familiarity with either Java or Scala would be helpful but not required.
Apache Mahout Sequences in Action - Sequencefiles provide an efficient mechanism for representing sequential or temporal data. Sequences can represent streams of any type of entity, but the focus is on time series data for business analytics scenarios. This example builds a simple recommender system that learns user preferences based on past interactions. User preferences change over time according to their observed ratings; each interaction updates the underlying model. Each recommendation session maintains a sliding window for the recent history.

These are just a few examples of the wide variety of open source machine learning projects that exist. With these resources and communities, anyone interested in machine learning can gain access to powerful techniques and knowledge without having to start from scratch. If you have an interest in machine learning and want to make a contribution, there are countless ways to get involved