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 […]
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.
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.
Machine learning is easily one of the biggest buzzwords in tech right now. Over the past three years, Google searches for “machine learning” have increased by over 350%. But understanding machine learning can be difficult — you either use pre-built packages that act like ‘black boxes’ where you pass in data and magic comes out […]
When I launched Dataquest a little under two years ago, one of the first things I did was write a blog post about why. At the time, if you wanted to become a data scientist, you were confronted with dozens of courses on sites like edX or Coursera with no easy path to getting a […]
Cleaning and preparing data is a critical first step in any machine learning project. In this blog post, Dataquest student Daniel Osei takes us through examining a dataset, selecting columns for features, exploring the data visually and then encoding the features for machine learning. After first reading about Machine Learning on Quora in 2015, Daniel […]
Learn how to build an end to end machine learning project — a key part of any data science portfolio — in this free tutorial walkthrough.
Kaggle competitions are a fantastic way to learn data science and build your portfolio. I personally used Kaggle to learn many data science concepts. I started out with Kaggle a few months after learning programming, and later won several competitions. Doing well in a Kaggle competition requires more than just knowing machine learning algorithms. It […]
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 […]
In this tutorial, we’ll guide you through the basic principles of machine learning, and how to get started with machine learning with Python.
Learn to predict sentiment in movie reviews with machine learning using naive bayes classifiction, Python, and scikit-learn.