Software, Machine Learning and more…

Welcome to Jekyll!
You’ll find this post in your _posts directory. Go ahead and edit it and re-build the site to see your changes. You can rebuild the site in many different ways, but the most common way is to run jekyll serve, which launches a web server and auto-regenerates your site when a file is updated.

Getting Started with octave
A brief introduction to Octave, a numerical tool that makes math much easier.

Introduction to Matplotlib
A brief introduction to Matplotlib
Introduction to Pandas
A brief introduction to pandas
Introduction to NumPy
A brief introduction to NumPy
Getting Started with Python 3
This is a brief introduction to Python 3, a scripting language widely used in data science and machine learning.

Como iniciarse en Machine Learning
Linear Support Vector Machine Classiffier
This notebook ilustrates how to use linear support vector machine for a binary classification task. We will be using the “wine” dataset provided by the Scikit-learn library.
Logistic Regression
This notebook helps us to introduce how to train a Logistic Regression model for binary classificacion and multiclass classification. We will use the “wine” dataset provided by the Scikit-learn library.
Learning Curves
In this notebook, we will show how learning curves behaves as a function of the training set size for models with high bias and high variance.
Training Linear Regression Models
In this notebook, we will review some techniques for training a linear regression model.
Classification task
This notebook contains steps and code to get started with classification tasks. We wil be using the Fashin-MNIST dataset, an alternative dataset to the well-known MNIST dataset
Common Steps on a Machine Learning Project
A sequence of common steps when working with a machine learning project
Introduction to Linear Regression With Python
Using the Auto dataset

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