After deploying your Python project (e.g., ML model, website) as a Flask web app on cloud solutions like Heroku, you may notice that it loads with an unsecured HTTP connection despite SSL/TSL certificates already configured.
Such unsecured requests pose a security concern because malicious actors can easily compromise communications between…
For every photo, there is more than meets the eye. The images taken with digital cameras and smartphones contain rich information (known as metadata) beyond the visible pixels.
This metadata can be helpful in many business cases. For instance, fraud detection systems for insurance claims analyze metadata of submitted photographs…
While machine learning (ML) concepts are essential, production engineering capabilities are the key to deploying and delivering value from ML models in the real world.
DeepLearning.AI and Coursera recently developed the MLOps Specialization course to share how to conceptualize, build, and maintain integrated ML systems.
In this article, I summarize…
DeepLearning.AI and Stanford HAI recently organized a virtual fireside chat with two of the world’s most eminent computer scientists — Andrew Ng and Fei-Fei Li.
Driven by a strong belief in healthcare’s social mission and importance to humanity, they have focused their efforts and expertise on the healthcare industry in…
Data is food for AI, and there is vast potential for model performance improvement by shifting from a model-centric to a data-centric approach. That is the motivation behind the recent Data-Centric AI Competition organized by Andrew Ng and DeepLearning.AI.
In this article, I share the techniques of my Top 5%…
Logistic regression is a highly effective modeling technique that has remained a mainstay in statistics since its development in the 1940s.
Given its popularity and utility, data practitioners should understand the fundamentals of logistic regression before using it to tackle data and business problems.
In this article, we explore the…
R is a powerful programming language that is explicitly designed for data science and analytics. Its open-source nature means plenty of free online resources are available for data practitioners to leverage for their projects.
In this article, we look at the most popular R-based data science repos on GitHub based…
We do not always have abundant data for our projects. Often, we only have one sample dataset to work with due to the lack of resources to perform repeated experiments (e.g. for A/B testing).
Fortunately, we have resampling methods to make the most of whatever data we have. Bootstrapping is…
The “language war” between Python and R is a longstanding contentious topic in data science that is still widely discussed today.
In reality, the best language is the one that best fits the specific job. …
Visualization of network graphs helps us better understand complex relationships between multiple entities.
Beyond static images, Python libraries such as Pyvis allow us to build highly interactive graphs for network visualization. …
Data Scientist @ AXA | Pharmacist | Master of Science (Business Analytics) www.linkedin.com/in/kennethleungty
