30-days-ml-

ai-with-python-harvard

anomaly-behavior-detection

Detecting a behavior anomaly The dataset below contains a location timeserie of a person living alone in their appartment. The data indicates in which location/room he was at which point in time. This data has been collected by sensors installed in the home. This person had a health incident in the night from 2019-09-18 to 2019-09-19, probably at 3:00 or 10:00 which shows in a drastic change in location behavior and resulted in the person going to hospital. By the sensor setup in the home, the location entrance and livingroom are really one single bigger room. Merge them. Explore the data and give an overview of key metrics (graphically and quantitatively) Can you say something about the living routines of the person? Propose one or more methods to detect the incident in "real time" by analyzing the location data. Real-time means, that while time passes more and more of the data gets "known" to your detection method. It can trigger as soon as the incident is detected, an action can be triggered. We are interested in understanding how you proceed in analyzing this case. Show your thought process What methods did you try and why What are their strength and weaknesses of the approaches. Are they robust and generalizable to other users? How do you test your code for correctness? We are looking forward to your propositions! PS: You are free to use other Python libraries as desired. Please return your Notebook as an answer.

anonymization-api

How to build and deploy an anonymization API with FastAPI

appliedtimeseriesanalysiswithpython

awesome-mlops

A curated list of references for MLOps

booking_extra_baggage

At eDreams ODIGEO we are always looking for ways to improve customer satisfaction. With this objective in mind, we would like to predict whether a new customer

cnn_computervision

cours

credit_risk_small_data_syntheticsamples_ensemblemethods

crispdm_ultime

data-evaluation

data-preparation

Data preparation is one of the most important and often time-consuming aspects of data mining.

data-science-competitions

Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).

data-understanding-

data_science_notes

Keep learning something new

datavisualization

Data Science Guide

deeplearning.ai-natural-language-processing-specialization

This repository contains my full work and notes on Coursera's NLP Specialization (Natural Language Processing) taught by the instructor Younes Bensouda Mourri and Łukasz Kaiser offered by deeplearning.ai

deploy-keras-model-in-production

Using Flask to deploy saved keras model in production. Flask based REST API to expose the prediction endpoints.

deploying-machine-learning-models

Example Repo for the Udemy Course "Deployment of Machine Learning Models"

deployment-heroku

Deployment Heroku

end-to-end-python-machine-learning-tutorial-for-beginner

In this end-to-end Python machine learning tutorial, you’ll learn how to use Scikit-Learn to build and tune a supervised learning model!

fastapi-model-deployment

An example for deploying Tensorflow 2 models with Docker and Fast API

fastapi-nlp

To showcase the features of building REST API's by FastAPI for Machine learning and Deep learning models

fastapi_nlp_docker

Deploy sentiment analyis with Fastapi

faster_rcnn_facedetection

Train Faster RCNN using Keras to detect face

fasterdatascienceeducationminicourses

finetune-distilbert

Huggingface transformers: Finetuning DistilBERT on a toxic comment binary classification task.

frauddetection_fastapi

this api will detect fraud

frauddetection_fastapi_vf