Build Machine Learning Web Application with Streamlit
Build Real World Anti-Money Laundering Machine Learning Application from Scratch to Detect Suspicious Transaction
Ratings 4.03 / 5.00
What You Will Learn!
- Machine Learning algorithms like Decision Trees, Random Forests
- Create Machine Learning Web Apps
- Machine Learning Performance Measures like Confusion Matrix, ROC/AUC, Precision-Recall Curve
- Crucial Streamlit Commands
Description
So you are a data scientist who has created great machine learning algorithms in python notebook. Now, you are thinking of developing an application based on that algorithm that can be shared with others or you want to capitalize by selling that application.
Even if you are just starting out in data science, you will reach a point, where you will look to build applications around your model.
Streamlit is the answer to your question, it is an open-source Python library that makes it very easy to create a web application based on machine learning and data for data science. It is easy to learn, you don’t need to understand all the concepts in web programming like HTML, CSS, javascript. Hence, you can build powerful machine learning web apps in a few lines of code at a great speed.
In this course we will cover the following:
1. Introduction to Streamlit
2. Installation of Streamlit
3. Concept of Money Laundering
4. Creating Machine Learning Web App from Scratch
5. Brief description of Machine Learning algorithms like Decision Trees and Random Forests
6. Short Introduction to performance measure of Machine Learning Models like Confusion Matrix, ROC Curve, Precision and Recall Curve
We will gain a lot of useful knowledge from this course. I hope to see you in this course.
Who Should Attend!
- Data Scientist who want to develop application based on their machine learning models
