6 machine learning resources for getting started

If you turned in for my recent webinar, Machine Learning: Principles and Practice, you may have heard me talking about some of my favorite machine learning resources, including recent white papers and some classic studies.

As I mentioned in the webinar, machine learning is not new. SAS has been pursuing machine learning in practice since the early 1980s. Over the decades, professionals at SAS have created many machine learning technologies and have learned how to apply machine learning to create value for organizations. This webinar series is one of many resources that can help you understand what machine learning is and how to use it.

SAS Resources:

Machine Learning with SAS Enterprise Miner: See how a team of SAS Enterprise Miner developers used machine learning techniques to predict customer churn in a famous telecom dataset.

An Overview of Machine Learning with SAS Enterprise Miner: This technical white paper includes SAS code examples for supervised learning from sparse data, determining the number of clusters in a dataset, and deep learning.

Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners: Written for corporate leaders, and technology and marketing executives, this book shows how organizations can harness the power of high performance computing architectures and data mining, text analytics, and machine learning algorithms.

External Resources:

Statistical Modeling: The Two Cultures: The grandfather of machine learning, Leo Breiman, outlines the fundamental ideas and philosophies of the discipline and discusses two different approaches to modeling and data analysis.

7 common mistakes of machine learning: Whether you’re a seasoned pro or a noob, machine learning is tricky. Save yourself some time by avoiding these common mistakes.

11 clever Methods of overfitting and how to avoid them: Probably the most common mistake in machine learning, and one of the hardest to avoid, is overfitting your training data. This article highlights some common (and not so common) practices that can lead to overfitting.