Last Updated on October 3, 2016 The 5 Most Common Mistakes That Beginners MakeAnd 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 learning. I firmly believe thatanyone can get started and do really wellwith applied machine learning. Hopefully, you can identify yourself […]

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 you can use them. And you will discover the advice Per has for beginners, like you, that are also looking to make the transition. And you […]

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 how to start solving real-world problems. The traditional approach to getting started in machine learning has a gap on the path to practitioner. The Machine […]

Last Updated on December 7, 2016 How Santhosh Sharma Went FromWorking in the Loans Department of a Bank toGetting Hired as a Senior Data Scientist at Target. Santhosh Sharma recently reached out to me to share his inspirational story and I want to share it with you. His story shows how with enthusiasm for machine learning, taking the initiative, sharing your results and a little luck can change your career and throw you deep into applied machine learning. After reading this interview, you will know: How Santhosh […]

Last Updated on February 19, 2017 Stories of how students and developers get started in applied machine learning are an inspiration. In this post, you will hear about Álvaro Lemos story and his transition from student to getting a machine learning internship. Including: How interest in genetic algorithms lead to the discovery of neural networks and the broader field of machine learning. How tutorial-based blog posts and books helped pass a test for a machine learning internship on a data science team Let’s […]

Last Updated on May 22, 2019 There is an important difference between classification and regression problems. Fundamentally, classification is about predicting a label and regression is about predicting a quantity. I often see questions such as: How do I calculate accuracy for my regression problem? Questions like this are a symptom of not truly understanding the difference between classification and regression and what accuracy is trying to measure. In this tutorial, you will discover the differences between classification and regression. […]

Last Updated on July 5, 2019 The superpower of machine learning is generalization. I recently got the question: “How can a machine learning model make accurate predictions on data that it has not seen before?” The answer is generalization, and this is the capability that we seek when we apply machine learning to challenging problems. In this post, you will discover generalization, the superpower of machine learning After reading this post, you will know: That machine learning algorithms all seek […]

Last Updated on July 13, 2019 Technical topics like mathematics, physics, and even computer science are taught using a bottom-up approach. This approach involves laying out the topics in an area of study in a logical way with a natural progression in complexity and capability. The problem is, humans are not robots executing a learning program. We require motivation, excitement, and most importantly, a connection of the topic to tangible results. Useful skills we use every day like reading, driving, […]

Last Updated on August 15, 2019 Machine learning is a large and interdisciplinary field of study. You can achieve impressive results with machine learning and find solutions to very challenging problems. But this is only a small corner of the broader field of machine learning often called predictive modeling or predictive analytics. In this post, you will discover how to change the way you think about machine learning in order to best serve you as a machine learning practitioner. After […]

The development environment that you use for machine learning may be just as important as the machine learning methods that you use to solve your predictive modeling problem. A few times a week, I get a question such as: What is your development environment for machine learning? In this post, you will discover the development environment that I use and recommend for applied machine learning for developers. After reading this post, you will know: The important distinctions between the role […]