Why start with Weka over another tool like the R environment or Python for applied machine learning? In this post you will discover why Weka is the perfect platform for beginners interested in rapidly getting good at applied machine learning. After reading this post you will know: Why getting started in applied machine learning is hard. […]
Keras is a deep learning library that wraps the efficient numerical libraries Theano and TensorFlow. In this post, you will discover how to develop and evaluate neural network models using Keras for a regression problem. After completing this step-by-step tutorial, you will know: How to load a CSV dataset and make it available to Keras […]
Finding an accurate machine learning model is not the end of the project. In this post you will discover how to save and load your machine learning model in Python using scikit-learn. This allows you to save your model to file and load it later in order to make predictions. Let’s get started. Update Jan/2017: […]
There are standard workflows in a machine learning project that can be automated. In Python scikit-learn, Pipelines help to to clearly define and automate these workflows. In this post you will discover Pipelines in scikit-learn and how you can automate common machine learning workflows. Let’s get started. Update Jan/2017: Updated to reflect changes to the […]
Ensembles can give you a boost in accuracy on your dataset. In this post you will discover how you can create some of the most powerful types of ensembles in Python using scikit-learn. This case study will step you through Boosting, Bagging and Majority Voting and show you how you can continue to ratchet up […]
Keras is a Python library for deep learning that wraps the efficient numerical libraries Theano and TensorFlow. In this tutorial, you will discover how to use Keras to develop and evaluate neural network models for multi-class classification problems. After completing this step-by-step tutorial, you will know: How to load data from CSV and make it […]
It is important to compare the performance of multiple different machine learning algorithms consistently. In this post you will discover how you can create a test harness to compare multiple different machine learning algorithms in Python with scikit-learn. You can use this test harness as a template on your own machine learning problems and add […]
Keras is one of the most popular deep learning libraries in Python for research and development because of its simplicity and ease of use. The scikit-learn library is the most popular library for general machine learning in Python. In this post, you will discover how you can use deep learning models from Keras with the […]
Spot-checking is a way of discovering which algorithms perform well on your machine learning problem. You cannot know which algorithms are best suited to your problem before hand. You must trial a number of methods and focus attention on those that prove themselves the most promising. In this post you will discover 6 machine learning […]
Spot-checking is a way of discovering which algorithms perform well on your machine learning problem. You cannot know which algorithms are best suited to your problem before hand. You must trial a number of methods and focus attention on those that prove themselves the most promising. In this post you will discover 6 machine learning […]
