The metrics that you choose to evaluate your machine learning algorithms are very important. Choice of metrics influences how the performance of machine learning algorithms is measured and compared. They influence how you weight the importance of different characteristics in the results and your ultimate choice of which algorithm to choose. In this post, you […]
You need to know how well your algorithms perform on unseen data. The best way to evaluate the performance of an algorithm would be to make predictions for new data to which you already know the answers. The second best way is to use clever techniques from statistics called resampling methods that allow you to […]
The data features that you use to train your machine learning models have a huge influence on the performance you can achieve. Irrelevant or partially relevant features can negatively impact model performance. In this post you will discover automatic feature selection techniques that you can use to prepare your machine learning data in python with […]
Many machine learning algorithms make assumptions about your data. It is often a very good idea to prepare your data in such way to best expose the structure of the problem to the machine learning algorithms that you intend to use. In this post you will discover how to prepare your data for machine learning […]
Artificial neural networks are a fascinating area of study, although they can be intimidating when just getting started. There is a lot of specialized terminology used when describing the data structures and algorithms used in the field. In this post, you will get a crash course in the terminology and processes used in the field of multi-layer […]
You must understand your data in order to get the best results from machine learning algorithms. The fastest way to learn more about your data is to use data visualization. In this post you will discover exactly how you can visualize your machine learning data in Python using Pandas. Let’s get started. Update Mar/2018: Added […]
You must understand your data in order to get the best results. In this post you will discover 7 recipes that you can use in Python to learn more about your machine learning data. Let’s get started. Update Mar/2018: Added alternate link to download the dataset as the original appears to have been taken down. […]
Keras is a Python deep learning library that provides easy and convenient access to the powerful numerical libraries like TensorFlow. Large deep learning models require a lot of compute time to run. You can run them on your CPU but it can take hours or days to get a result. If you have access to […]
You must be able to load your data before you can start your machine learning project. The most common format for machine learning data is CSV files. There are a number of ways to load a CSV file in Python. In this post you will discover the different ways that you can use to load […]
Two of the top numerical platforms in Python that provide the basis for Deep Learning research and development are Theano and TensorFlow. Both are very powerful libraries, but both can be difficult to use directly for creating deep learning models. In this post, you will discover the Keras Python library that provides a clean and […]
