Скачать или смотреть Create an AI-Powered Movie Recommendation App from Scratch | Python, NumPy, Pandas, ReactJS, Flask

AI is revolutionizing industries, from media 🎬 and entertainment, to healthcare 🏥, automobiles 🚗, fintech, and beyond. It’s reshaping the future of technology, and you’re still early to catch this trend. Now is the perfect time to build your first AI and machine learning movie recommendation app 🌟.

In this tutorial, we’ll guide you step-by-step through the process of building a complete AI-powered movie recommendation system using Python, NumPy, Pandas, ReactJS, and Flask. With a stunning responsive front end, our app connects seamlessly to the backend, leveraging a machine learning model to fetch personalized movie recommendations 🍿.

What you’ll learn:
•Python for AI development: Master the fundamentals, including variables, data types, control flow, functions, file handling, and object-oriented programming.
•NumPy: Crucial for scientific computing, handling arrays, and optimizing performance 🔢.
•Pandas: Ideal for data manipulation, cleaning, and exploratory data analysis (EDA) using DataFrames 📊.
•ReactJS: Build a dynamic front-end for seamless user experience and interaction with AI-powered services 💻.
•Flask: Learn to create a robust backend, connecting your AI model to deliver real-time recommendations 🛠️.

Materials / References:
Github Codes (give it a star ⭐):
Frontend code - https://github.com/mendsalbert/cinema...
Model code - https://github.com/mendsalbert/cinema...
Backend code - https://github.com/mendsalbert/cinema...
Visual Studio website - https://code.visualstudio.com/
Python official website - https://www.python.org/
-omdbapi key - https://www.omdbapi.com/apikey.aspx
Datasets - https://drive.google.com/drive/folder...

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Github: https://github.com/mendsalbert

Business inquiries:
email: [email protected]

Timestamps
00:00:00 - Intro
00:00:24 - Setting up Environment
00:01:13 - Python
00:21:30 - Numpy
00:28:53 - Pandas
00:36:04 - Project Setup
00:37:05 - Model
00:46:40 - Backend
00:48:28 - Frontend
00:50:42 - Conclusion