Embark on a comprehensive end-to-end machine learning project implementation, covering everything from dataset analysis to deployment using Docker and GitHub Actions. Learn to prepare and analyze datasets, train models, evaluate performance, and make predictions. Master essential tools like Git, VS Code, and Flask for web application development. Explore deployment strategies using Heroku and Docker, gaining practical experience in the entire machine learning pipeline. Perfect for aspiring data scientists and machine learning engineers looking to build real-world projects and enhance their skills in model development, version control, and deployment automation.
End to End Machine Learning Project Implementation with Dockers, GitHub Actions and Deployment
Krish Naik via YouTube
Overview
Syllabus
Understanding the dataset
Preparing Dataset And Basic Analysis
Preparing Dataset For Model Training
Training The Model
Performance Metrics
Prediction Of New Data
Pickling the model file
Setting Up Github And VS Code
Tools And Software Required
Creating A New Environment
Setting up Git
Creating A FLASK Web Application
Running An Testing our application
Prediction From Front End Application
Procfile for Heroku Deployment
Deploying The App To Heroku
Deploying The App Using Dockers
Taught by
Krish Naik
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