A complete overview on how to build your machine learning model, and deploy it using Django REST API

Introduction

Let consider the following scenario: you have implemented an outstanding machine learning model that predicts if a patient is suffering or not from paludism. Then hospitals in your city want to integrate your model into their systems for general use. But those systems have been developed in completely different programming…

This article is a comprehensive overview of collecting data from Twitter using tweepy

Introduction

Getting data comes as the second step in any data science/machine learning project lifecycle, right after framing the problem you want to solve, which would make this step be the backbone of the rest of the phases. Also, social media are great places to collect data, especially for competitor analysis…

This article is a comprehensive overview of understanding the challenges related to the efficient management of the machine learning lifecycle, and how mlflow efficiently tackles those challenges.

Introduction

Machine learning experimentation results are challenging to reproduce. Without detailed tracking, the same results might not be reproducible from one team to another…

This article is a comprehensive overview of a step-by-step guide to deploy a simple sentiment analysis application using streamlit and Docker

Introduction

As Machine Learning or Software Engineer, we would like to share our applications/models and their dependencies with others. This process, if not done properly can cost us not only time but also money. Nowadays, one of the most preferred ways to collaborate with other developers is by using source code…

This article is a comprehensive overview of the different metrics for evaluating binary classification models and some strategies to choose the right one for your use case.

Introduction

As a Data Scientist, it is critical that you don’t build your machine learning model in a vacuum, you must always be asking…

This article is a comprehensive overview of some simple Image Augmentation techniques

Research has demonstrated the efficiency of Deep Convolutional Neural networks for many Computer Vision tasks in many domains such as autonomous vehicles, medical imaging, etc. However, these networks are heavily reliant on big quality data in order to perform remarkably well on real world problems. But, how big can a…

This article is a guide for better understanding transfer learning with intuitive examples.

Training a deep neural network from scratch is not an easy task, and might not always be the right thing to do because of the complexity that could go with the different tasks such as finding the right amount the data for the problem to be solved, preprocessing, and designing…

This article is a comprehensive overview of some numerical representations of text data for Machine Learning algorithms

A) Introduction

Building machine learning models is not only restricted to numbers, we might want to be able to work with text as well. However, those models can only be fed with numbers. To bridge this gap a lot of research has gone into creating numerical representation for text data. In this…

Zoumana Keita

Love Doing Data Things 😀

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