Product managers need to make trade-offs and considerations when building products that support machine learning (ML). Different product use cases require different ML models. Therefore, the core principle of learning how to manage ML models is the key product manager skill set.
Enabling machine learning (ML) products has an ongoing collection, cleansing and analysis of data loops for input into ML models. This repetitive loop is the driving force behind the ML algorithm and enables ML products to provide useful insights for users.
When it comes to machine learning, it's important to find the next problem to solve. Data scientists and ML engineers have limited resources. Choosing the wrong project for your team is not only costly, but it also undermines morale, customer trust, and product failure.
This article will explain the following: 1. What is overfitting in machine learning projects? 2. How do we detect overfitting? 3. How do we solve the overfitting problem?
I will tell you which machine learning model to use based on the nature of the problem, I will try to explain some concepts.
This article is a machine learning science for all, involving all the key knowledge points related to machine learning.
A quick tutorial on k-means clustering and principal component analysis (PCA).
When we want to quickly distinguish between tagged data, it's easy to ignore unsupervised learning. Unsupervised machine learning is inherently powerful, and clustering is by far the most common of these types of problems.
The machine learning model is called "black box" by many people. This means that while we can get accurate predictions from it, we cannot clearly explain or identify the logic behind these predictions. But how do we extract important insights from the model? What to remember and what features or tools do we need to implement? These are important issues that come to mind when presenting model interpretability questions.
Take marketing as an example. Today's marketers are working hard to deliver relevant information to customers. Although humans cannot communicate with a large number of customers on a large scale, machines can. Not sure what it looks like in practice? In this article, I will explain five key uses of machine learning in marketing.