Predict diabetes using machine learning models. Experiment with logistic regression, decision trees, and random forests to achieve accurate predictions based on health indicators. Complete lifecycle of ML project included.

In this project, the model is save and reused for prediction. Also, it is being containerize with docker to be ready for deployment.

Tool to simplify the collection of datasets from openml.org. Includes support for timeouts, filtering and local caching of files, and for running tests against multiple datasets. Allows both random and reproducible tests. Supports reading from cache when openml.org or the internet is unavailable. Multiprocessing is supported for faster compariso…

Accurate image classification powered by InceptionV3 deep learning model. Quickly classify diverse images with high precision using TensorFlow.

MLU is a modular ML toolkit resembling lodash, streamlining from data prep to deployment with chainable utility functions. It enhances ML workflows, seamlessly integrates with top frameworks, and supports efficient data handling and model evaluation. Open-source, MLU welcomes contributions to foster innovation and efficiency in the ML community.

The ideal way to evaluate binary classification models.

Computer vision project for classifying American sign language.

A machine learning project for predicting credit card approvals using logistic regression.

Machine learning models for sentiment analysis.

A PyTorch project implementing neural style transfer using the VGG19 model, combining the content of one image with the style of another for artistic transformations.

This performs of evaluation of machine learning models with ease and provides the visualization

Predict sentiment of text as positive or negative using Python's NLTK and scikit-learn.

Word2Vec implementation using tensorflow

Case study of multi-layer perceptron and random forest techniques as applied to a subset of the GQA dataset.

This model is a basic example of a feed-forward neural network (also known as a multi-layer perceptron) that could be used for binary classification problems.

Phishing website detection with machine learning

Perform Machine Learning models evaluation for classification and detection of ML libraries

A lambda to invoke inference requests to AWS SageMaker endpoints, using test data previous created. Helping with monitoring model performance.

A model, trained to classify dog breeds based on a dataset of images using a convolutional neural network (CNN) with TensorFlow and Keras.