Udemy – Traffic Forecasting with Python: LSTM & Graph Neural Network 2024-11

Udemy – Traffic Forecasting with Python: LSTM & Graph Neural Network 2024-11 Downloadly IRSpace

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Apr 18, 2025 - 17:53 Updated: Apr 21, 2025 - 17:38
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Udemy – Traffic Forecasting with Python: LSTM & Graph Neural Network 2024-11
Udemy – Traffic Forecasting with Python: LSTM & Graph Neural Network 2024-11

Traffic Forecasting with Python: LSTM & Graph Neural Network Course. This course is an in-depth journey into the world of advanced time series forecasting, specifically for traffic data analysis using Python. Throughout the course, learners will work with the real-world PeMSD7 traffic speed dataset to build predictive models that can predict traffic conditions with high accuracy. The course focuses on combining long-term short-term memory (LSTM) networks with graph convolutional networks (GCN) to enable learners to understand and apply advanced techniques in spatio-temporal data analysis.

Key topics include data preprocessing, feature engineering, model building, and evaluation, along with hands-on coding in Python to reinforce understanding. Learners will also gain hands-on experience using popular libraries such as TensorFlow and Keras for deep learning applications.

What you will learn in this course

  • Understanding and analyzing real-world traffic data using Python
  • Implementation and application of graph convolutional networks (GCN) for traffic data
  • Combining LSTM networks with GCN for time series prediction
  • Preprocessing and normalizing large datasets for machine learning
  • Build, train, and evaluate predictive models using TensorFlow and Keras
  • Visualizing and interpreting model results for traffic forecasting

This course is suitable for people who:

  • Data scientists and machine learning engineers interested in time series forecasting
  • Python programmers looking to improve their skills in deep learning and graph-based models
  • Researchers and students in the fields of transportation, urban planning, or smart cities
  • Professionals working with traffic data or other spatiotemporal datasets
  • Artificial intelligence enthusiasts who seek to understand and implement advanced neural network architectures such as LSTM and convolutional graph networks
  • People with a data analytics background who want to apply machine learning to real-world datasets

Course details: Traffic Forecasting with Python: LSTM & Graph Neural Network

  • Publisher:  Udemy
  • Lecturer:  Karthik Karunakaran, Ph.D.
  • Training level: Beginner to advanced
  • Training duration: 1 hour and 8 minutes
  • Number of lessons: 38

Course topics

 Traffic Forecasting with Python: LSTM & Graph Neural Network

Prerequisites for the Traffic Forecasting with Python: LSTM & Graph Neural Network course

  • Basic proficiency in Python programming.
  • Access to a computer with an internet connection for coding and data analysis.

Course images

Traffic Forecasting with Python: LSTM & Graph Neural Network

Sample course video

Installation Guide

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Subtitles: None

Quality: 720p

Download link

Download file – 154 MB

File(s) password: www.downloadly.ir

File size

154 MB

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