This article focuses on demystifying the difference between traditional data analytics methods vs. machine-learning-driven ones, not without providing firstly a clear understanding of what is — and what is not — data analytics compared to other data terms often used interchangeably. After gaining such understanding, the post provides clear and succinct guidelines on when to […]
Unity makes strength. This well-known motto perfectly captures the essence of ensemble methods: one of the most powerful machine learning (ML) approaches -with permission from deep neural networks- to effectively address complex problems predicated on complex data, by combining multiple models for addressing one predictive task. This article describes three common ways to build ensemble […]
It’s easy enough to make poor decisions in your machine learning projects that derail your efforts and jeopardize your outcomes, especially as a beginner. While you will undoubtedly improve in your practice over time, here are five tips for avoiding common rookie mistakes and cementing your project’s success to keep in mind while you are […]
Machine learning (ML) models contain numerous adjustable settings called hyperparameters that control how they learn from data. Unlike model parameters that are learned automatically during training, hyperparameters must be carefully configured by developers to optimize model performance. These settings range from learning rates and network architectures in neural networks to tree depths in decision forests, […]
Are you looking to make a career in machine learning? If so, this guide is for you. Machine learning is an interesting field with a lot of potential to solve real-world problems. However, going from a novice to a professional requires a structured approach that not only focuses on technical skills but also on understanding […]
Whether you implement a neural network yourself or you use a built in library for neural network learning, it is of paramount importance to understand the significance of a sigmoid function. The sigmoid function is the key to understanding how a neural network learns complex problems. This function also served as a basis for discovering […]
Machine learning is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence. The focus of the field is learning, that is, acquiring skills or knowledge from experience. Most commonly, this means synthesizing useful concepts from historical data. As such, there are many different types of […]
Supervised machine learning is often described as the problem of approximating a target function that maps inputs to outputs. This description is characterized as searching through and evaluating candidate hypothesis from hypothesis spaces. The discussion of hypotheses in machine learning can be confusing for a beginner, especially when “hypothesis” has a distinct, but related meaning […]
Do you have questions like: What data is best for my problem? What algorithm is best for my data? How do I best configure my algorithm? Why can’t a machine learning expert just give you a straight answer to your question? In this post, I want to help you see why no one can ever […]
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 […]
