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: […]
Time series is different from more traditional classification and regression predictive modeling problems. The temporal structure adds an order to the observations. This imposed order means that important assumptions about the consistency of those observations needs to be handled specifically. For example, when modeling, there are assumptions that the summary statistics of observations are consistent. […]
Time series forecasting is a difficult problem. Unlike classification and regression, time series data also adds a time dimension which imposes an ordering of observations. This turns rows into a sequence which requires careful and specific handling. In this post, you will discover the top books for time series analysis and forecasting in R. These […]
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, […]
Logistic regression is the go-to linear classification algorithm for two-class problems. It is easy to implement, easy to understand and gets great results on a wide variety of problems, even when the expectations the method has of your data are violated. In this tutorial, you will discover how to implement logistic regression with stochastic gradient […]
The core of many machine learning algorithms is optimization. Optimization algorithms are used by machine learning algorithms to find a good set of model parameters given a training dataset. The most common optimization algorithm used in machine learning is stochastic gradient descent. In this tutorial, you will discover how to implement stochastic gradient descent to […]
5-Steps To Get Started and Get Good at Machine Learning I teach a 5-step process that you can use to get your start in applied machine learning. It is unconventional. The traditional way to teach machine learning is bottom-up. Start with the theory and math, then algorithm implementations, then send you off to figure out […]
Per Went From Developer To Machine Learning Practitioner, And So Can You! Per Borgen is an inspiration. He transitioned from developer to machine learning practitioner. And he explained how he did it. In this post, you will discover the lessons learned by Per on his transition. You will discover two methodologies he adopted and how […]
The 5 Most Common Mistakes That Beginners Make And How To Avoid Them. I help beginners get started in machine learning. But I see the same mistakes in both mindset and action again and again. In this post, you will discover the 5 most common ways that I see beginners slip-up when getting started in machine […]
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 […]
