Collection of tensorflow notebooks tutorials for implementing some basic Deep Learning architectures.

Jupyter Notebook that can classify and visualize text files using Machine Learning

The repository is a part of the IBM Data Science Capstone project. The project includes the segmentation and clustering of Neighbourhoods in Toronto using K Means Machine Learning Clustering algorithm. To view the notebook, visit this website.

machine learning notebook

A notebook about commonly used machine learning algorithms.

This repository is made following the course by Sir Jose Portilla, and focuses on unsupervised Machine Learning algorithms. I studied all these concepts in January 2024

In the series of Python notebooks I am going to practice solving questions with common data science and machine learning techniques and I am going to share the notebooks here.

Data Science portfolio of ipython Notebooks with several Machine Learning algorithms. They follow a structured data science methodology to face various challenges

In this notebook, I compared two famous clustering algorithm, the minibatchkmeans and the regular kmeans on cellular image dataset.

Data analysis and data science notebooks

Here you will find a Notebook with examples of various Machine Learning algorithms (ML), more specifically, Supervised and Unsupervised Learning examples. All of the code is followed by explanations and everything is easy to use and to understand thanks to the documentation.

Image Retrieval system Python implementation based on Bag of Visual Words and Clustering

A Concise Notebook to Summarize Your iMessage Conversations

This Repository provides an Illustrative NoteBook for implementing KMean from scratch.

A series of 12 assignments/labs regarding Stochastic Processes and Machine Learning including a plethora of models and techniques implemented in Google Colab notebooks

Jupyter notebooks implementing Machine Learning algorithms in Scikit-learn and Python

This notebook focuses on the classification of Iris Species by its Sepal Length, Sepal Width, Petal Length and Petal Width.