Notebooks from the Machine Learning Specialization

The full collection of Jupyter Notebook labs from Andrew Ng's new Machine Learning Specialization.

Machine Learning Engineer Nanodegree portfolio, which includes projects and their notebooks/reports.

Notebook used to explore and classify 500,000 tweets about Elon Musk in an unsupervised manner.

This repo helps keep track about exercises, jupyter notebooks and datasets on the introduction to machine learning (pytorch) udacity nanodegree program.

The notebook with the experiments to replicate and enhance the stock clustering proposed by Han(2022) for alogtrading, with KMeans Optimization

This Repository consist of some popular Machine Learning Algorithms and their implementation of both theory and code in Jupyter Notebooks

Notebooks for the ML course in Medellin

Collection of Jupyter notebooks with examples of machine learning - supervised, unsupervised and reinforcement learning models.

A notebook about commonly used machine learning algorithms.

This Repo contains Notebook for Anomaly Detection using Dimensionality Reduction techniques

A notebook using many unsupervised learning techniques. PCA, K-means, Gaussian Mixtures. Clustering, dimensionality reduction, anomaly detection

Bunch of notebooks collection from Kaggle competitions.

Applying Unsupervised Learning techniques to identify customer segments hidden in the data

Contains various Machine Learning projects in python (jupyter-notebook).

It is a Unsupervised Machine Learning Algorithm. In this notebook we have to predict the optimum number of clusters in Iris dataset and represent it visually.

This repo features Python-based Jupyter notebooks covering data science techniques like data cleaning, EDA, statistical modeling, ML, and visualization. With detailed explanations, code snippets, and visualisations, it's a comprehensive guide for both beginners and experienced data scientists.

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.

implemented by jupyter notebook