Machine Learning

How to Better Understand Your Machine Learning Data in Weka

Last Updated on August 22, 2019 It is important to take your time to learn about your data when starting on a new machine learning problem. There are key things that you can look at to very quickly learn more about your dataset, such as descriptive statistics and data visualizations. In this post you will discover how you can learn more about your data in the Weka machine learning workbench my reviewing descriptive statistics and visualizations of your data. After […]

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Machine Learning

Object Classification with CNNs using the Keras Deep Learning Library

Last Updated on August 27, 2020 Keras is a Python library for deep learning that wraps the powerful numerical libraries Theano and TensorFlow. A difficult problem where traditional neural networks fall down is called object recognition. It is where a model is able to identify the objects in images. In this post, you will discover how to develop and evaluate deep learning models for object recognition in Keras. After completing this tutorial you will know: About the CIFAR-10 object classification […]

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Machine Learning

How to Predict Sentiment From Movie Reviews Using Deep Learning (Text Classification)

Last Updated on August 27, 2020 Sentiment analysis is a natural language processing problem where text is understood and the underlying intent is predicted. In this post, you will discover how you can predict the sentiment of movie reviews as either positive or negative in Python using the Keras deep learning library. After reading this post you will know: About the IMDB sentiment analysis problem for natural language processing and how to load it in Keras. How to use word […]

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Machine Learning

How to Normalize and Standardize Your Machine Learning Data in Weka

Last Updated on December 11, 2019 Machine learning algorithms make assumptions about the dataset you are modeling. Often, raw data is comprised of attributes with varying scales. For example, one attribute may be in kilograms and another may be a count. Although not required, you can often get a boost in performance by carefully choosing methods to rescale your data. In this post you will discover how you can rescale your data so that all of the data has the […]

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Machine Learning

Applied Deep Learning in Python Mini-Course

Last Updated on December 11, 2019 Deep learning is a fascinating field of study and the techniques are achieving world class results in a range of challenging machine learning problems. It can be hard to get started in deep learning. Which library should you use and which techniques should you focus on? In this post you will discover a 14-part crash course into deep learning in Python with the easy to use and powerful Keras library. This mini-course is intended for […]

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Machine Learning

Crash Course in Recurrent Neural Networks for Deep Learning

Last Updated on August 14, 2019 There is another type of neural network that is dominating difficult machine learning problems that involve sequences of inputs called recurrent neural networks. Recurrent neural networks have connections that have loops, adding feedback and memory to the networks over time. This memory allows this type of network to learn and generalize across sequences of inputs rather than individual patterns. A powerful type of Recurrent Neural Network called the Long Short-Term Memory Network has been […]

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Machine Learning

How to Transform Your Machine Learning Data in Weka

Last Updated on December 13, 2019 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 post you will know: How to convert a real valued attribute into a discrete distribution called discretization. How to […]

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Machine Learning

How To Handle Missing Values In Machine Learning Data With Weka

Last Updated on December 13, 2019 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 reading this post you will know: How to mark missing values in your dataset. How to remove data with […]

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Machine Learning

Deep Learning Courses

Last Updated on August 19, 2019 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 can browse and work through to develop and cement your understanding of the field. This is a long post […]

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Machine Learning

How to Perform Feature Selection With Machine Learning Data in Weka

Last Updated on December 13, 2019 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 with your machine learning data in Weka. After reading this post you will know: About the importance of feature […]

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