Last Updated on August 28, 2020 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 more and different algorithms to compare. Kick-start your project with my new book Machine Learning Mastery With Python, including […]

Last Updated on August 28, 2020 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 the accuracy of the models on your own datasets. Kick-start your project with my new book Machine Learning Mastery […]

Last Updated on August 28, 2020 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. Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples. Let’s get started. Update Jan/2017: Updated […]

Last Updated on August 28, 2020 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. Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples. […]

Last Updated on August 3, 2020 From Developer to Machine Learning Practitioner in 14 Days Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step […]

Last Updated on August 21, 2019 You should pick the right tool for the job. The specific predictive modeling problem that you are working on should dictate the specific programming language, libraries and even machine learning algorithms to use. But, what if you are just getting started and looking for a platform to learn and practice machine learning? In this post, you will discover that Python is the growing platform for applied machine learning, likely to outpace and topple R […]

Last Updated on August 21, 2019 Linux is an excellent environment for machine learning development with Python. The tools can be installed quickly and easily and you can develop and run large models directly. In this tutorial, you will discover how to create and setup a Linux virtual machine for machine learning with Python. After completing this tutorial, you will know: How to download and install VirtualBox for managing virtual machines. How to download and setup Fedora Linux. How to […]

Last Updated on September 17, 2020 It can be difficult to install a Python machine learning environment on some platforms. Python itself must be installed first and then there are many packages to install, and it can be confusing for beginners. In this tutorial, you will discover how to set up a Python machine learning development environment using Anaconda. After completing this tutorial, you will have a working Python environment to begin learning, practicing, and developing machine learning and deep […]

Last Updated on August 21, 2019 It can be difficult to install a Python machine learning environment on Mac OS X. Python itself must be installed first, and then there are many packages to install, and it can be confusing for beginners. In this tutorial, you will discover how to setup a Python 3 machine learning and deep learning development environment using macports. After completing this tutorial, you will have a working Python 3 environment to begin learning, practicing, and […]

Last Updated on January 10, 2020 Test datasets are small contrived datasets that let you test a machine learning algorithm or test harness. The data from test datasets have well-defined properties, such as linearly or non-linearity, that allow you to explore specific algorithm behavior. The scikit-learn Python library provides a suite of functions for generating samples from configurable test problems for regression and classification. In this tutorial, you will discover test problems and how to use them in Python with […]