Let's talk about machine learning and deep learning

[Ehsanuls55]

First, let's take a quick visual look at the relationship between all of these concepts.



At its core, machine learning is nothing more than a “prediction model.” It has (a) data that it learns from, and (b) an algorithm that does the actual learning.

The algorithm is nothing more than a set of rules that tell the code what to expect (data about X or Y) and what to do with it.

The quality of a machine learning algorithm is everything when it comes to determining its usefulness. If the rules are illogical or too limited, it cannot provide useful information.

It's easy to feel intimidated by the overwhelming technical depth of this field (decision trees, reinforcement learning, and Bayesian networks are just a few of the many areas), but you'll be fine if you remember this:

**Machine learning is, fundamentally, a set of rules for making sense of incoming data

If you want to create a tool that learns GPS routes to help drivers, it has to know the laws of one-way roads. Otherwise, it might start learning really fast routes that aren't as convenient as they seem at first glance.

However, when the rules reflect a deep and nuanced understanding of each variable at play, machine vp manufacturing production email list learning can do the seemingly impossible.

Traditionally, providing accurate time estimates has been one of the most challenging parts of a project manager’s job. However, many are surprised to find that machines are capable of performing at a comparable level. ClickUp is currently testing the ML feature with several of our users to predict what actions an individual is likely to perform. This enables task predictions that, over time, are able to mimic human characteristics, such as subjective task estimation, accurately enough to be extremely useful.

This approach speeds up the feedback loop and we have seen teams move from limited semi-automated actions to fully automated ones in just a few weeks. Some elements that our algorithms can achieve are:

Predict and assign tasks to the right team members
Automatically tag users in comments that are relevant to them
Display notifications and updates based on their relevance to a particular user
Predict and determine when deadlines will not be met, and correct the estimated duration of tasks.