Data Science

Welcome to the Data Science repository! Here, you will find resources, examples, and links to repositories related to the field of data science. If you're interested in learning about data analysis techniques, machine learning, and neural network models, this repository is the right place for you.

At the end of the repository, you can find some simple examples to get started with machine learning algorithms, here.

Links to Related Repositories

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	💥 Computer Vision 💥 Here, you will find implementations of popular algorithms and models used in Computer Vision tasks, such as object detection, facial recognition, image segmentation, image classification, and more. In addition, you will find tutorials, notebooks, and additional resources to help you understand fundamental concepts and apply advanced techniques in this field. See details
	💥 Natural Language Processing 💥 In this repository, we will explore techniques and algorithms used to understand, analyze, and generate natural language. From information extraction and sentiment analysis to machine translation and text generation, we will tackle a variety of challenges in natural language processing. See details
	💥 Playing with ML algorithms 💥 At the end of this repository, you will find various machine learning techniques applied to video games. Some of the techniques we can find include: reinforcement learning, search trees, and genetic algorithms. See details

Simple Examples of Neural Networks

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	Simple Perceptron: In this link, you will find a complete development of the simple perceptron from scratch. This is a machine learning algorithm that functions as a binary classifier. It is based on a single processing unit called a perceptron, which takes a set of inputs and produces a binary output. The perceptron applies weights to each input and sums them along with a threshold to determine the final output. See details
	MNIST: This repository contains an implementation of the famous MNIST dataset problem in Python. MNIST is a widely used dataset in the field of computer vision and machine learning. It consists of a set of 60,000 handwritten digit images for training and 10,000 additional images for testing. See details