How to create a Pandas dataframe

The massive versatility of Pandas means that you can create dataframes from almost any type of raw data. Whether you have a list, a list of lists, a dictionary, a...

How to create a collaborative filtering recommender system

Recommender systems, or recommendation engines as they’re also known, are everywhere these days. Whether you’re looking for books on Amazon, tracks on Spotify, movies on Netflix or a date on...

How to build the 'Hotdog , not Hotdog' image classifier

Convolutional Neural Networks or CNNs are one of the most widely used AI techniques for detecting complex features in data. They’re particularly good for image recognition, and are used in...

How to create a neural network for sentiment analysis

Sentiment analysis, or opinion mining, is a form of emotion AI and uses natural language processing and computational linguistics to analyse text and infer the sentiment. Sentiment analysis has loads...

How to use GAPandas to view your Google Analytics data

Over the past decade I’ve written more Google Analytics API queries than I can remember. Initially, I favoured PHP for these (and still do for permanent web-based applications utilising GA...

How to use your GPU to accelerate XGBoost models

If you’re not fortunate enough to have a really powerful deep learning machine for your work, one of the problems you’ll likely face is that your models can take quite...

How to use scikit-learn datasets in data science projects

The scikit-learn package comes with a range of small built-in toy datasets that are ideal for using in test projects and applications. As they’re part of the scikit-learn package, you...

How to use Python regular expressions to extract information

Regular expressions are used for pattern matching in programming, allowing you to identify or extract very specific pieces of text from a string or document. They’re very powerful and extremely...

How to use mean encoding in your machine learning models

When you’re building a machine learning model, the feature engineering step is often the most important. From your initial small batch of features, the clever use of maths and stats...