I love streaming algorithms - they make you think! Since they can process data in (near) real-time, they are ideal for applications where immediate feedback is needed. Most of today's recommendation or machine learning systems are built on batch process, however.
Could be Interesting! Big Data Data ScienceAs I was contemplating on how to best display the dashboard for stock analysis, I came across this Nuts and Bolts of Chart Types - I like its sarcastic tone, and to be fair, some of them have truth to it. I am going to leave it here for my future reference.
Could be Interesting! Data ScienceWhile their transformed data may not be used for further data analysis, T-SNE and UMAP (non-linear) dimensionality reduction techniques provide great values for data visualization!
Data ScienceValues of the features or variables in the dataset are not the same. Even if they are numerical values, their ranges can be quite dramatic. Ensuring that all features are treated equally in terms of scale and range is essential for the performance and stability of many machine learning algorithms.
Data ScienceWhen dealing with high dimensional data (known as The Curse of Dimensionality), it is often useful to reduce the data size for efficient computation. One such method, also very popular method, is Principal Component Analysis.
Data ScienceWe all have our preferred learning style. For me, when it comes to learning, I am more of a read, learn and do person. I prefer reading the materials than watching an hour long video. I believe reading gives me time to absorb and digest the knowledge, then, think of the potential implications. However, learning without an implementation is just an entertainment. So, I built and wired up this site to reflect my learnings. (Utilized Flask Framework, containerized with Docker and first deployed on AWS in Jan 2023).
Disclaimers: By no means, my write-ups are absolute truth. There are many different ways to achieve something. Besides, technology is always evolving and some of my posts might be outdated but fundamentals stay the same.