Different machine learning algorithms are searching for different trends and patterns. Consequently, one algorithm isn’t the best across all datasets or for all use-cases. To find the best solution, we conduct a lot of experiments, evaluating different machine learning algorithms and tuning their hyper-parameters. This post introduces various important topics: …

The purpose of the Data Preparation stage is to get the data into the best format for machine learning, this includes three stages: Data Cleansing, Data Transformation, and Feature Engineering. …

Quality data is fundamental to any data science engagement. To gain actionable insights, the appropriate data must be sourced and cleansed. There are two key stages of Data Understanding: a Data Assessment and Data Exploration. Data Assessment The first step in data understanding is a Data Assessment. This should be undertaken before…

Introduction to AI Fairness — AI Fairness is an important topic for machine learning practitioners. We must be aware that there can be both positive and negative implications for users when they interact with our models. Although our metric of success tends to be a performance metric (e.g. accuracy), those that interact with our models…

When comparing a machine learning approach with the current solution, I wish to understand if any observed difference is statistically significant; that it is unlikely to be simply due to chance or noise in the data. The appropriate test to evaluate statistical significance varies depending on what your machine learning…

This post is designed to give some guidance for evaluating the use of machine learning to solve your business problem. As a data scientist, I am highly motivated to find the “best” model — how close to perfect can I get my predictions to be? However, more often than not…

This post is designed to be an overview highlighting three of the most common deep learning architectures: Multi-layer Perceptrons, Convolutional Neural Networks and Recurrent Neural Networks. The content in this post is high-level, introducing the main features of each architecture and describing the type of data it tends to be…

What is Deep Learning? To answer this question we should first introduce various related terms Artificial Intelligence (AI) AI is where computers are able to perform tasks that normally require human intelligence. True AI, or pure AI, refers to machines that are equal in intelligence to humans. …

This post is part of a series introducing Algorithm Explorer: a framework for exploring which data science methods relate to your business needs. The introductory post “Machine Learning: Where to begin…” can be found here and Algorithm Explorer here If you are looking to use machine learning to help make…