📘 This repository offers a complete K-Nearest Neighbors (KNN) tutorial, guiding you from core theory to hands-on practice. Learn to implement KNN from scratch with NumPy, apply it using scikit-learn, and explore visualizations, datasets, and Jupyter notebooks to fully understand, test, and optimize the algorithm.

This repository contains jupyter notebooks of assignments and tutorials used in the course introduction to data science in python, the first course in Applied Data Science using Python Specialization from University of Michigan offered by Coursera.

The objective of this repository is to show how I perform optimization of photonics structure. Some Jupyter Notebook contains tutorials and exemples with my codes and how I use them. The goal of this project is not to offer complete tools for optimization but to suggere some ways to perform it.

This repository contains jupyter notebooks of assignments and tutorials used in the course Data Science in Python, from Amit Mistry offered by Udemy

This repo provides a collection of Jupyter notebooks demonstrating various functionalities and applications of the Pandas library, and it offers practical examples for data manipulation, analysis, and visualization.

In this tutorial, learn how to create a Jupyter Notebook that contains Python code for defining logistic regression, then use TensorFlow (tf.keras) to implement it. The Notebook runs on IBM Cloud Pak® for Data as a Service on IBM Cloud®. The IBM Cloud Pak for Data platform provides additional support, such as integration with multiple data sources, built-in analytics, Jupyter Notebooks, and machine learning. It also offers scalability by distributing processes across multiple computing resources. You can choose to create assets in Python, Scala, and R, and use open source frameworks (such as TensorFlow) that are already installed on the IBM Cloud Pak for Data as a Service platform.