This article is a continuation of my previous one in which we were dealing in brief about what Machine Learning is, what are the techniques that we have in machine learning, etc. Here, we will be working with the Workflow of machine learning. Click here to start with machine learning on Microsoft Azure. Machine learning follows a flowchart or a Workflow with all the topics given. Question for the model This is the first very basic step that plays a major role in machine learning.
Machine learning engineers are the key role player in machine learning model development. They are responsible for multiple task from coding to deployment, testing and troubleshooting the issues comes while developing such models. However, to know what exactly machine learning engineer do you need to can find out their role, duties and actual task performed by such professionals. Sometimes Machine learning engineers also called data scientist as they study and transform the data science prototypes and algorithms while working on ML-based modles. Similarly, there are many other responsibilities machine learning engineers perform.
Machine learning involves the use of machine learning algorithms and models. For beginners, this is very confusing as often "machine learning algorithm" is used interchangeably with "machine learning model." Are they the same thing or something different? As a developer, your intuition with "algorithms" like sort algorithms and search algorithms will help to clear up this confusion. In this post, you will discover the difference between machine learning "algorithms" and "models."
Machine Learning (ML) and Artificial Intelligence (AI) are the most difficult domains in terms of developing and implementing. There are a lot of pre and post processes involved while implementing a machine learning based solution to solve even the simplest problem. In general, machine learning implementation involves the following steps. The above four steps seem very simple in theory, however, when you try to implement these, a lot of resources and limitations catch your attention to achieve the desired outcome. This is, however, the part where we are simply trying to implement our solution to achieve the desired accuracy and we have not even yet touched the application part of what our solution is capable of offering.
Do you get what overfitting means in machine learning? If you don't, then you better learn about it if you want to use or leverage machine learning. Because overfitting can ruin the effectiveness of machine learning. I wrote this blog because I found existing explanations of overfitting to be too technical. I hope this one is more consumable by non specialists. Machine learning involves a fairly complex workflow, see Machine Learning Algorithm!