Tag: Machine Learning
21 Places to Find Free Datasets for Data Science Projects
A collection of the best places to find free data sets for data visualization, data cleaning, machine learning, and data processing projects.
Read MoreNew Course: Learn Linear Modeling in R
Learn the fundamentals of modeling with R and build a foundation for machine learning as you work through building, fitting, and assessing linear models.
Read MorePython Machine Learning Tutorial: Predicting Airbnb Prices
Learn about machine learning in Python and build your very first ML model from scratch to predict Airbnb prices using k-nearest neighbors.
Read MoreThe 10 Best Machine Learning Algorithms for Data Science Beginners
Machine learning algorithms are key for anyone who’s interested in the data science field. Here’s an introduction to ten of the most fundamental ML algorithms.
Read MoreData Cleaning and Preparation for Machine Learning
Learn data cleaning for a machine learning project by cleaning and preparing loan data from LendingClub for a predictive analytics project.
Read MoreTutorial: Text Classification in Python Using spaCy
Learn text classification using linear regression in Python using the spaCy package in this free machine learning tutorial.
Read MoreAn Intro to Deep Learning in Python
Deep learning is a type of machine learning that’s growing at an almost frightening pace. Nearly every projection has the deep learning industry expanding massively over the next decade. This market research report, for example, expects deep learning to grow 71x in the US and more than that globally over the next ten years. There’s […]
Read MoreTutorial: Understanding Regression Error Metrics in Python
Error metrics are short and useful summaries of the quality of our data. We dive into four common regression metrics and discuss their use cases.
Read MoreTop 20 Python AI and Machine Learning Open Source Projects
Getting into Machine Learning and AI is not an easy task, but is a critical part of data science programs. Many aspiring professionals and enthusiasts find it hard to establish a proper path into the field, given the enormous amount of resources available today. The field is evolving constantly and it is crucial that we […]
Read More18 Inspiring Women in Data Science, AI, Big Data, and Machine Learning
Women are underrepresented in STEM fields – science, technology, engineering, and math. For instance, women made up 27% of people employed in computer and mathematical occupations in 1960. But instead of growing over several decades, as many more women participated in the workforce overall, that number had declined to 26% by 2013, according to a […]
Read MoreTutorial: Learning Curves for Machine Learning in Python
This Python data science tutorial uses a real-world data set to teach you how to diagnose and reduce bias and variance in machine learning.
Read MoreKaggle Fundamentals: The Titanic Competition
Kaggle is a site where people create algorithms and compete against machine learning practitioners around the world. Your algorithm wins the competition if it’s the most accurate on a particular data set. Kaggle is a fun way to practice your machine learning skills. This tutorial is based on part of our free, four-part course: Kaggle […]
Read MoreBuilding a Data Science Portfolio: Machine Learning Project
Learn how to build an end to end machine learning project — a key part of any data science portfolio — in this free tutorial walkthrough.
Read MorePython for data science: Getting started
Python is becoming an increasingly popular language for data science, and with good reason. It’s easy to learn, has powerful data science libraries, and integrates well with databases and tools like Hadoop and Spark. With Python, we can perform the full lifecycle of data science projects, including reading data in, analyzing data, visualizing data, and […]
Read MoreTutorial: Learn Python Programming and Machine Learning
In this tutorial, we’ll guide you through the basic principles of machine learning, and how to get started with machine learning with Python.
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