Machine learning models are parameterized so that their behavior can be tuned for a given problem. Models can have many parameters and finding the best combination of parameters can be treated as a search problem. In this post, you will discover how to tune the parameters of machine learning algorithms in Python using the scikit-learn […]
Not all data attributes are created equal. More is not always better when it comes to attributes or columns in your dataset. In this post you will discover how to select attributes in your data before creating a machine learning model using the scikit-learn library. Update: For a more recent tutorial on feature selection in Python see the […]
Your data must be prepared before you can build models. The data preparation process can involve three steps: data selection, data preprocessing and data transformation. In this post you will discover two simple data transformation methods you can apply to your data in Python using scikit-learn. Update: See this post for a more up to […]
Before you can build machine learning models, you need to load your data into memory. In this post you will discover how to load data for machine learning in Python using scikit-learn. Update March/2018: Added alternate link to download the dataset as the original appears to have been taken down. Packaged Datasets The scikit-learn library […]
Before you can select and prepare your data for modeling, you need to understand what you’ve got to start with. If you’re a using the Python stack for machine learning, a library that you can use to better understand your data is Pandas. In this post you will discover some quick and dirty recipes for […]
If you are using the Python stack for studying and applying machine learning, then the library that you will want to use for data analysis and data manipulation is Pandas. This post gives you a quick introduction to the Pandas library and point you in the right direction for getting started. Data Analysis In Python […]
You have to get your hands dirty. You can read all of the blog posts and watch all the videos in the world, but you’re not actually going to start really get machine learning until you start practicing. The scikit-learn Python library is very easy to get up and running. Nevertheless I see a lot […]
This is a guest post by Adrian Rosebrock from PyImageSearch, a blog all about computer vision, image processing, and building image search engines. Take a look at the Where’s Waldo puzzle above. How long does it take you to find Waldo? 10 seconds? 30 seconds? Over a minute? Waldo is the ultimate game of hide […]
If you get serious with data analysis and machine learning in python then you will make good use of IPython notebooks. In this post we will review some takeaway points made by Fernando Perez, the creator of IPython in a keynote presentation at SciPy 2013. The title of the talk was IPython: from the shell to […]
The Python conference PyCon2014 has held recently and the videos for the conference are online. I have been working my way through the interesting machine learning ones and will share a few on this over the coming weeks. A great talk if you are starting out in data science or machine learning in python was […]
