Machine Learning Weekly #5

By Richmond Alake

Hybrid by Vladyslav Ociacia. (Image Licence held by the author)

I’ve been in the machine learning industry professionally for a year now. I’ve noticed that I have a strong sense of importance the more closely my work and learning are aligned with the frontier technology.

Perhaps I’m overstretching the feeling of doing what you love as a job. Regardless of emotions, the truth is I have learnt a lot within a year, and I’m excited by how much more AI-based content is waiting to be explored.

This Week

• I recently shared the key traits I’ve identified within outstanding Data Scientist and Machine Learning practitioners that I’ve come across over the past three years, both in academic institutions or within professional environments. Have a read and find out if you already have the mentioned traits
• Last week I continued my algorithm exploration journey by exploring Merge sort, an efficient algorithm to sort large dataset.

Tip of the week

Data Scientists need to define technical problems appropriately. A well thought out problem definition phase of a project saves time, energy and effort.

Problem definitions are clear statements describing the initial state of a problem that’s to be solved. These statements indicate problem properties such as the task…