Machine Learning is a set of computational techniques that allows us to extract patterns from data. In other words, these algorithms can learn the information contained in datasets like tabular data, text, images, or even videos. However, there is no universal algorithm capable of learning every pattern from every kind of data. Each machine learning algorithm solves a specific type of learning problem. We call machine learning tasks the particular learning problems solved by machine learning algorithms.
In this post, we list useful datasets from universities, governments and companies from which you can download datasets for you machine learning project.
Attributes in different scales are common in Machine Learning projects. For example, a medical record dataset can include in the columns weight, height, and blood pressure. These attributes have different units of measure and vary in different intervals, making their comparison difficult. In these cases, we can apply a process called scaling to make this comparison easier. … Continue reading How to scale attributes with normalization and standartization
In this post, we discuss how big and complex projects are demotivating, and how we can overcome the demotivation building smaller and simpler projects.
This page contains a list of links to small machine learning projects, which you can use as examples or to bootstrap a more significant projects.
In this post, we present Linear Regression analysis using a one variable example. Although real Linear Regression models use multiple input variables, the idea here is to keep the examples as simple and straightforward as possible, so you can focus on the intuition behind Linear Regression and don't get confused with too much data preparation or other details about tools and data manipulation. There are many algorithms to perform regression analysis, but Linear Regression is the simplest of them and the recommended algorithm to start with.
Google Colab is the right tool to get started with data science. You don't need to install software, you have proper documentation, and you can share your work easily. Int this article, we list five reasons that make Google Colab the right tool for beginner data scientists.
In this tutorial, we are going to create our first machine learning model using the most famous Python libraries and the Google Colab environment, so we don't have to waste any time installing and configuring new software.
Markov Decision Process is a fundamental concept in the Reinforcement Learning. In this post we selected more than 40 resources about Markov Decision Process, including blog posts, books, and videos.
Learn how to create random maps for the FrozenLake environment.