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. Let’s get started. Update: For a more recent tutorial on feature selection in […]
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. Let’s get started. Update: See this post for a […]
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. Let’s get started. Update March/2018: Added alternate link to download the dataset as the original appears to have been taken down. Packaged Datasets […]
Data Origami is a new website by Cameron Davidson-Pilon that provides data science screencasts. It is a cool idea and a cool site. Cameron was kind enough to give me access to the site so that I could review it. I watched all of the videos I could and wrote up all my notes, and […]
Julia Evans wrote a post recently titled “Machine learning isn’t Kaggle competitions“. It was an interesting post because it pointed out an important truth. If you want to solve business problems using machine learning, doing well at Kaggle competitions is not a good indicator of that skills. The rationale is that the work required to […]
Online communities are invaluable in machine learning, regardless of your skill level. The reason is that, like programming, you never stop learning. You simply cannot know everything, there are always new algorithms, new data and new combinations to discover and practice. Communities help. You can get your questions answered, learn by answering other peoples questions […]
R is a powerful platform for data analysis and machine learning. It is my main workhorse for things like competitions and consulting work. The reason is the large amounts of powerful algorithms available, all on the one platform. In this post I want to point out some resources you can use to get started in […]
David Mimno is an assistant professor in the Information Sciences department at Cornell University. He has a background and interest in Natural Language Processing (NLP), specifically topic modeling. Notably, he is the chief maintainer of MALLET, the Java-based NLP library. I recently came across a blog post by David titled “Advice for students of machine […]
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. Let’s get started. Data […]
