An 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 […]
Learning Curves for Machine Learning
Diagnose Bias and Variance to Reduce Error When building machine learning models, we want to keep error as low as possible. Two major sources of error are bias and variance. If we managed to reduce these two, then we could build more accurate models. But how do we diagnose bias and variance in the first […]
Machine Learning Fundamentals: Predicting Airbnb Prices
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 […]
The Six Elements of the Perfect Data Science Learning Tool
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 […]
Preparing and Cleaning Data for Machine Learning
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 […]
Building a data science portfolio: Machine learning project
This is the third in a series of posts on how to build a Data Science Portfolio. You can find links to the other posts in this series at the bottom of the post. Data science companies are increasingly looking at portfolios when making hiring decisions. One of the reasons for this is that a […]
How to Get Into the Top 15 of a Kaggle Competition Using Python
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 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 […]
Tutorial: Machine Learning with Python
In this tutorial, we’ll guide you through the basic principles of machine learning, and how to get started with machine learning with Python.
Tutorial: K Nearest Neighbors in Python
In this post, we’ll be using the K-nearest neighbors algorithm to predict how many points NBA players scored in the 2013-2014 season. Along the way, we’ll learn about euclidean distance and figure out which NBA players are the most similar to Lebron James. If you want to follow along, you can grab the dataset in […]
Tutorial: Natural Language Processing with Python
Python has some powerful tools that enable you to do natural language processing (NLP). In this tutorial, we’ll learn about how to do some basic NLP in Python. Looking at the data We’ll be looking at a dataset consisting of submissions to Hacker News from 2006 to 2015. The data was taken from here. Arnaud […]
Tutorial: Predicting Movie Review Sentiment with Naive Bayes
Sentiment analysis is a field dedicated to extracting subjective emotions and feelings from text. One common use of sentiment analysis is to figure out if a text expresses negative or positive feelings. Written reviews are great datasets for doing sentiment analysis because they often come with a score that can be used to train an […]
Tutorial: K-Means Clustering US Senators
Clustering is a powerful way to split up datasets into groups based on similarity. A very popular clustering algorithm is K-means clustering. In K-means clustering, we divide data up into a fixed number of clusters while trying to ensure that the items in each cluster are as similar as possible. In this post, we’ll explore […]
