Knowledge graphs are network graphs that link related concepts and properties together to create a form of inferencing engine, with knowledge engineering being the programming aspect of graph usage. Explore how knowledge graphs are created and queried, how they are used as part of a broader form of enterprise metadata management, and how they tie into ML and the IoT.
Gerrymandering has long been a problem in the process of choosing elected officials globally, but it is especially a problem in the United States, where… Read More »Can Gerrymandering Be Ended via Machine Learning?
Hyperparameter tuning is considered time-consuming and computationally expensive as it requires testing numerous combinations before attaining the optimum values. Using the available tuning libraries do… Read More »Keras Hyperparameter Tuning Has Never Been That Easy: When AI Facilitates Building Another AI
AI is taking over. In 2021, we’re going to see machine learning taking on a bigger role in data analysis as algorithms become capable of… Read More »Essential Machine Learning Algorithms That Data Analysts Need To Know
The continued evolution of technology has empowered humanity quite unlike anything else, offering tools and technologies to assist us in our daily endeavors. Take machine… Read More »The Different Ways in Which Machine Learning Makes Healthcare Better
The continued evolution of technology has changed our lives in more profound ways than we’d ever been able to imagine. Think about it — we… Read More »Machine Learning: How it is Transforming the Education Sector
Rationalizing the effort of our intensive care staff by predicting patient condition indicators in real time is a challenge because of an impressive number of… Read More »Horowitz Index Day-Ahead Change Prediction in Real Time
A few years ago, it was extremely uncommon to retrain a machine learning model with new observations systematically. This was mostly because the model retraining… Read More »When Should a Machine Learning Model Be Retrained?
A summary of the book “Introduction to Statistical Learning” in jupyter notebooks Whenever someone asks me “How to get started in data science?”, I usually… Read More »Statistical Machine Learning in Python
Introduction Almost every machine learning algorithm comes with a large number of settings that we, the machine learning researchers and practitioners, need to specify. These… Read More »Cross-validation and hyperparameter tuning
Interesting picture summarizing several types of techniques used in machine learning, contrasting unsupervised learning with unsupervised learning and reinforcement learning. The difference between supervised and… Read More »Machine Learning with Applications in One Picture
