Time series prediction problems are a difficult type of predictive modeling problem. Unlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables. A powerful type of neural network designed to handle sequence dependence is called a recurrent neural network. The Long Short-Term Memory network or LSTM network […]
It is really important to have a performance baseline on your machine learning problem. It will give you a point of reference to which you can compare all other models that you construct. In this post you will discover how to develop a baseline of performance for a machine learning problem using Weka. After reading […]
Time Series prediction is a difficult problem both to frame and address with machine learning. In this post, you will discover how to develop neural network models for time series prediction in Python using the Keras deep learning library. After reading this post, you will know: About the airline passengers univariate time series prediction problem […]
The problem of predictive modeling is to create models that have good performance making predictions on new unseen data. Therefore it is critically important to use robust techniques to train and evaluate your models on your available training data. The more reliable the estimate of the performance on your model, the further you can push […]
A big benefit of using the Weka platform is the large number of supported machine learning algorithms. The more algorithms that you can try on your problem the more you will learn about your problem and likely closer you will get to discovering the one or few algorithms that perform best. In this post you will […]
It is hyperbole to say deep learning is achieving state-of-the-art results across a range of difficult problem domains. A fact, but also hyperbole. There is a lot of excitement around artificial intelligence, machine learning and deep learning at the moment. It is also an amazing opportunity to get on on the ground floor of some really powerful tech. […]
Raw machine learning data contains a mixture of attributes, some of which are relevant to making predictions. How do you know which features to use and which to remove? The process of selecting features in your data to model your problem is called feature selection. In this post you will discover how to perform feature selection […]
It can be difficult to get started in deep learning. Thankfully, a number of universities have opened up their deep learning course material for free, which can be a great jump-start when you are looking to better understand the foundations of deep learning. In this post you will discover the deep learning courses that you […]
Data is rarely clean and often you can have corrupt or missing values. It is important to identify, mark and handle missing data when developing machine learning models in order to get the very best performance. In this post you will discover how to handle missing values in your machine learning data using Weka. After […]
Often your raw data for machine learning is not in an ideal form for modeling. You need to prepare or reshape it to meet the expectations of different machine learning algorithms. In this post you will discover two techniques that you can use to transform your machine learning data ready for modeling. After reading this […]
