End To End Machine Learning | Euron
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Ready to build your data science skills from scratch to advanced levels?
This comprehensive video series is your ultimate guide to mastering end-to-end data science projects. Perfect for beginners and professionals alike, we'll cover everything from Python fundamentals to implementing industry-ready machine learning, deep learning, NLP, and computer vision projects.
Here's what you'll learn:
How to create fully functional end-to-end pipelines for data science projects.
Key concepts of MLOps, including tools like Flask, Streamlit, and FastAPI.
Deployment strategies on cloud platforms like AWS, Azure, and GCP.
Modular coding techniques for efficient and scalable project development.
Practical examples with real-world datasets such as Wine Quality Prediction.
This hands-on series is designed to help you kickstart your programming journey or refine your coding skills. You'll learn to build robust, industry-standard workflows that you can showcase on your resume.
Don't miss this opportunity to learn and grow in the field of data science. Hit play and code along! Like, Subscribe, and hit the notification bell to stay updated with the latest tutorials from EuronTech. Let's master Python and data science together! 🧑💻
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CHAPTERS:
00:00 - Introduction
01:26 - Learning Objectives
02:45 - Series Overview
07:38 - Implementation Prerequisites
08:51 - Wine Quality Prediction Problem
10:28 - Creating GitHub Repository
13:05 - Project Folder Structure Setup
22:16 - Importance of pathlib
23:54 - Implementing Logging
25:47 - Final Project Structure
27:25 - Project Setup Process
27:48 - Creating Virtual Environment
28:49 - Installing Required Packages
30:10 - Local Package Setup
32:18 - Custom Logger Implementation
35:50 - Writing Utility Functions
36:00 - Understanding Utility
38:45 - Introduction to ConfigBox
42:54 - Ensuring Annotations
45:05 - Project Workflow Overview
47:17 - Jupyter Notebook Implementation
56:31 - Jupyter Notebook Experimentation
57:43 - Data Ingestion Process
59:31 - Data Ingestion Techniques
1:04:34 - Entity and Configuration Manager
1:07:34 - Creating Configuration Manager
1:08:34 - Data Ingestion Component
1:09:30 - Creating Data Pipeline
1:10:58 - Model Reuse Implementation
1:17:45 - Data Validation Techniques
1:28:55 - Data Transformation Process
1:31:31 - Data Transformation Pipeline
1:35:19 - Model Training Overview
1:39:23 - Model Trainer Implementation
1:43:20 - Model Evaluation Techniques
1:50:00 - Creating Prediction Pipeline
1:51:34 - Web Application Development
1:57:34 - Project Deployment
2:02:27 - Understanding CI/CD Deployment
2:06:09 - Problem Solution Overview
2:08:10 - High-Level Deployment Architecture
2:09:07 - Creating IAM User
2:10:56 - ECR Repository Setup
2:11:38 - EC2 Instance Creation
2:14:58 - Configuring EC2 as Self-Hosted Runner
2:17:19 - Adding Security Credentials
2:19:43 - CI/CD Pipeline Overview
2:20:42 - Continuous Integration Explained
2:21:09 - Continuous Deployment Explained
2:22:04 - Testing Procedures
2:22:44 - Environment Destruction
2:24:16 - Final Thoughts
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