Hey guys, I made an Emotion-Detector project that analyzes customer feedback in text format to detect emotions using a pre-trained Hugging Face transformer model. It's designed to help businesses understand customer sentiment.
More details, check out the project here.
This is the demo video of the mini-project:
I just google and read some articles,also asked Grok, Gemini, ChatGpt to search together, and I organized the following 2 table for top supervised and unsupervised models people commonly used.
I've just read a good article from machine learning mastery.com ( https://machinelearningmastery.com )
You can check this article: A Practical Guide to Choosing the Right Algorithm for Your Problem: From Regression to Neural Networks
It talks about how you should choose the ML model in the analysis.
https://machinelearningmastery.com/practical-guide-choosing-right-algorithm-your-problem
When working on Python projects, managing dependencies properly is crucial. Installing packages globally without a virtual environment can lead to conflicts between projects requiring different package versions. This is where Python's virtual environments come in handy.
Dependency Isolation – Each project gets its own set of dependencies, preventing conflicts.
Reproducibility – Ensures that your project runs with the same dependencies on different machines.
Avoids System Pollution – Prevents unnecessary global installations that could interfere with system Python.
Easier Collaboration – Teammates can install only the required dependencies by using a requirements.txt file.
Machine Learning (ML) is a powerful tool that can help solve many real-world problems, from predicting stock prices to recognizing faces in images. However, for a model to work effectively, there’s a systematic process to follow. This process is known as the Machine Learning Workflow. Whether you're building your first ML model or just getting familiar with the process, understanding this workflow is crucial.
In
this post, we'll walk through the steps involved in an ML workflow, starting
from defining the problem to maintaining the model in production. By the end,
you'll have a good understanding of the basics of ML development.
No matter who you want to review docker or if you are new to docker.
Docker
is an open platform for developing, shipping, and running applications. Docker
enables you to separate your applications from your infrastructure so you can
deliver software quickly. (Dockerdocs:
https://docs.docker.com/get-started/docker-overview/ )
If this is your 1st time reaching Docker, you probably still don’t know what you can do with Docker by this definition.
Let me explain it. Have you had an experience when you shared your applications with your colleagues, teammates, or others, and they told you the application could not run on their laptops? It’s common problem – “I can run it on my computer.” The reasons behind this could be different operating systems, dependencies, etc.
Docker is an existence solving the problem mentioned above. It helps you to package and run your application in an isolated environment named “container”. Simply put, you can think of a container containing everything you need to run the application.
Hey guys, I made an Emotion-Detector project that analyzes customer feedback in text format to detect emotions using a pre-trained Hugging...
