Notebooks for detection and classification model training. Insect classification model. Python scripts for processing of data, collected with the Insect Detect DIY camera trap.

Explore a collection of Jupyter notebooks that guide you through various stages of the machine learning pipeline. From data analysis and feature engineering to model training and deployment, these notebooks provide practical insights for both beginners and experienced data enthusiasts. Let's dive into the world of data-driven decision-making! 📊🚀"

Predict loan defaults using ML. Leverage Logistic Regression, Random Forest, XGBoost. Preprocess data, train models, analyze features. Make informed lending decisions. Jupyter Notebook and code.

In this notebook we analyze and classify news articles using machine learning techniques, including Logistic Regression, Naive Bayes, Support Vector Machines, and Random Forests. Explore text vectorization and NLP for accurate news categorization.

Repository demonstrating how to train a custom CNN model based on yolo-v4-tiny architecture. This can be utilized for image classification, image localization or object detection applications.

Collection of Jupyter notebooks and python code files explaining the essentials of Pytorch.

An ML experiment repository analyzing Indian defense news articles. Includes the notebooks and dataset. Contributions welcome.

In this project, we will use Google Colab for model training and run the Tensorflow1.15 own object detection model. Colab is a free Jupyter Notebook environment hosted by Google that runs on the cloud. Google Colab provides free access to GPUs (Graphical Processing Units) and TPUs (Tensor Processing Units).