jupternotebook

Bootcamp online analista de dados disponibilizado pelo IGTI – Instituto de Gestão e Tecnologia da Informação

Aula de Inteligência Artificial - IFSP

Registro das atividades realizadas em aula.

University teaching files

DeepFake Detection Web-App[Mirage Breaker] 🖥 using Deep Learning(ResNext and LSTM), Flask and ReactJs where you can predict whether a video is FAKE Or REAL along with the confidence ratio.

VSCODE integration in jupyter-lab

End-to-End Movie Studio Analytics using SQL, MySQL, Python, Pandas and Matplotlib with Data Visualization and Business Insights.python sql mysql pandas numpy matplotlib data-analysis eda jupyter-notebook portfolio

Learn machine learning with Python through data exploration, visualization, and key libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.

Stroke Disease Prediction classifies a person with Stroke Disease and a healthy person based on the input dataset. This package can be imported into any application for adding security features. Input data is preprocessed and is given to over 7 models, where a maximum accuracy of 99.4% is achieved.

In this repository i am gonna walk you through evolution of face recognition algorithms using deep learning approach.

Top Machine Learning Projects | Best Machine Learning Projects With Source Code

My blog where I talk about Machine learning extensively. Build with fastpages

Quantifying spike trains using 3 types of rate codes and also Coefficient of variance of Interspike Intervals

🔄 A practical guide to bridging SAS and Python using SASPy inside WSL on Ubuntu, integrated with SAS OnDemand for Academics. Includes step-by-step setup, Jupyter notebook usage, and Python script automation for SAS reporting.

This repository contains a collection of Python programs which i am learning via Apna college youtube channel + Python libraries learned from different resources.

Multi-Class Image Classification Project to classify images into driving license, social security, and others category.

RAG 技术要点、本地实践（Ollama），支持 md 和 ipynb 两种文档格式。要点：通用的 索引、检索、生成，以及高级索引特性。高级特性包含：查询（重写、拆解、后退、HYDE）、索引（多特征索引、RAPTOR）、检索（ReRank）、生成（Self-RAG）、评估（RAGAS、deepeval、指标）

Here, you will find the explanation of Data Structures and Algorithms for Machine Learning Models using Python.

This repository showcases diverse machine learning projects, including: #Dimensional Reduction: Techniques for reducing feature space. #Ensembles: Methods like Gradient Boosting. #Supervised ML: Time series analysis and predictive modeling. #Unsupervised ML: Clustering and pattern discovery.

Using PySpark, I performed the ETL process on a large dataset (170,000 rows) of Video Games. Next, I created an AWS relational database instance & transformed the data to be loaded into PostgreSQL. Once in PgAdmin, exported the Video Game Review Table as a CSV file. Afterward, I loaded the data into Python to create Dataframes using Pandas. Then…