Ethereum Development. My motivation: There is a chance that crypto is the next big thing after the internet and AI. Maybe it is time to learn about smart contracts and crypto development. I am listing Ethereum here since it is the most popular, think market cap, but I am open to learning other protocols such as Polkadot, Solana, Algorand, Elrond, Waves, etc. I'd do some research, but pick the one with cleaner documentation to avoid any analysis paralysis.
Clustering Ensembles and Subspace Clustering. My motivation: When clustering high dimensional data, we have to apply some dimensionality reduction first or else the clustering algorithm will not work. I was thinking, why not iteratively pick some random dimensions, apply clustering algorithms using those feature subsets, and apply another layer of clustering on those clustering outputs. Then I stumbled upon the Clustering Ensembles and the Subspace Clustering concepts.
Neural Search. My motivation: Not sure about this one. Justing listing it here. Probably will only learn it once I need it somehow, otherwise will skip it.
Apache Spark / PySpark. At a certain scale, data doesn't fit in memory anymore, and one needs to learn to use Spark to build their machine learning solutions at scale.
Non-fiction Writing I want to learn how to write engaging non-fiction. Some authors have a certain rythem and their words sounds like poetry. Some authors know how to incorporate elements of fiction, a la storytelling, into non-fiction.