Linear regression is a prediction method that is more than 200 years old. Simple linear regression is a great first machine learning algorithm to implement as it requires you to estimate properties from your training dataset, but is simple enough for beginners to understand. In this tutorial, you will discover how to implement the simple […]
We cannot know which algorithm will be best for a given problem. Therefore, we need to design a test harness that we can use to evaluate different machine learning algorithms. In this tutorial, you will discover how to develop a machine learning algorithm test harness from scratch in Python. After completing this tutorial, you will […]
The goal of predictive modeling is to create models that make good predictions on new data. We don’t have access to this new data at the time of training, so we must use statistical methods to estimate the performance of a model on new data. This class of methods are called resampling methods, as they […]
5 Best Practices For Operationalizing Machine Learning. Not all predictive models are at Google-scale. Sometimes you develop a small predictive model that you want to put in your software. I recently received this reader question: Actually, there is a part that is missing in my knowledge about machine learning. All tutorials give you the steps up […]
The super bundle is awesome. It shows practical steps to start getting results with clear advice on how to take action and what to do when faced with a problem. The books got me very excited to work on projects and is perhaps the best resource I have seen online on Machine Learning
XGBoost With Python Mini-Course. XGBoost is an implementation of gradient boosting that is being used to win machine learning competitions. It is powerful but it can be hard to get started. In this post, you will discover a 7-part crash course on XGBoost with Python. This mini-course is designed for Python machine learning practitioners that […]
Gradient boosting is one of the most powerful techniques for applied machine learning and as such is quickly becoming one of the most popular. But how do you configure gradient boosting on your problem? In this post you will discover how you can configure gradient boosting on your machine learning problem by looking at configurations […]
The easy, practical, comprehensive machine learning book for newbie interested in this filed. Your top down approach always right for me.
This is another excellent book. The explanations are concise, very well written. Using real-world data like Otto from Kaggle is definitely much needed to learn ML. The codes are very well explained. I don’t see this book as merely a how-to tutorial, it’s a very noble cause by disseminating your knowledge and skill to empower […]
Machine Learning Mastery with R is a great book for anyone looking to get started with machine learning. The book gives details how each step of a machine learning project should go: from descriptive statistics, to model selection and tuning, to predictions. These details are not very technical however, which allows anyone who does not […]
