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Ready to build and deploy your own ML-powered book recommender system?
This comprehensive video is perfect for beginners and advanced learners alike, offering practical, step-by-step guidance to master machine learning and deployment essentials.
🔑 What you'll learn:
- How to build a collaborative filtering book recommender system from scratch.
- Master Python fundamentals and utilize libraries like pandas, NumPy, and scikit-learn.
- Learn to preprocess, clean, and transform datasets for machine learning.
- Understand the concepts behind K-nearest neighbors and recommendation algorithms.
- Deploy your ML model as a fully functional web application using Streamlit and Docker.
- Discover how to run your project on AWS with EC2 for real-world deployment.
✨ Perfect for:
- Beginners eager to kickstart their programming journey.
- Developers looking to enhance their machine learning and deployment skills.
- Anyone passionate about creating personalized recommendation systems.
📌 Why watch?
This video is packed with practical examples, clear explanations, and hands-on coding. Follow along as we walk you through every step of building, training, and deploying a book recommender system using Python. Plus, we’ll cover useful tips for managing production environments and collaborating effectively.
🎯 Call-to-action:
Hit play and code along to transform your skills! Don’t forget to Like, Subscribe, and hit the notification bell to stay updated on more tutorials. Start your journey to mastering machine learning and deployment today!
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CHAPTERS:
00:00 - Intro
02:14 - Agenda
05:35 - What is a Recommender System
08:03 - Content Based Recommender System
10:36 - Collaborative Filtering Techniques
15:18 - How to Create a Recommendation System
17:00 - Dataset Overview
19:53 - Creating a GitHub Repository
24:05 - Setting Up the Development Environment
28:14 - Installing Required Libraries
29:53 - Creating Folder and Jupyter Notebook
33:51 - Importing and Understanding Data
42:50 - Fetching Data from URL
45:43 - Loading User Dataset
47:50 - Loading Book Ratings Dataset
52:09 - Analyzing User Ratings
1:04:47 - Creating Pivot Table for Collaborative Filtering
1:09:22 - Importance of Ratings in Recommendations
1:11:18 - Creating Pivot Table with Pandas
1:16:03 - Testing the Recommendation Model
1:18:37 - Retrieving Book Names
1:25:39 - Getting Book Poster URLs
1:40:49 - Creating Project Folder Structure
1:42:05 - Automating Project Folder Structure with Python
1:44:40 - Setting Up Config Folder
1:45:10 - Creating Constants Folder
1:47:03 - Folder Structure Creation with Python
1:53:03 - Purpose of template.py
1:55:45 - Setting Up Local Package for Project
2:01:14 - Real-Time Development Practices
2:03:18 - Installing Required Packages
2:03:55 - Exception Handling in Python
2:07:28 - Custom Exception Implementation
2:12:40 - Creating Custom Logger
2:15:11 - Utility Functions
2:17:52 - Defining Constants
2:27:19 - Writing the Configuration Entity
2:28:55 - Implementing Config Entity in Python
2:34:08 - Developing Data Ingestion Component
2:39:35 - Data Ingestion Process
2:44:30 - Data Validation Techniques
2:47:30 - Data Validation Review
2:52:54 - Additional Data Validation Steps
2:58:19 - Data Transformation Strategies
3:02:51 - Model Training Process
3:03:07 - Building a Web App
3:12:26 - Dockerizing the Project
3:18:01 - Deploying Project on AWS EC2
3:31:36 - Pushing Docker Image to Docker Hub
3:38:57 - Clean Up Process
3:39:01 - Gathering Feedback
3:40:00 - Conclusion
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