AI+ Sustainability™

Course code: AS2010

Price of the certification exam is included in the price of the course.

Formerly known as AI+ Sustainability™

Accelerate Sustainability with AI for smarter, greener progress

Drive Sustainable Innovation: Harness the Power of Advanced AI
AI for Greener Decisions: Explore carbon footprint analytics, resource optimization, and climate-impact modelling.
Strategic Sustainability Impact: Learn to design data-driven, eco-focused frameworks that support long-term environmental goals.
Future-Ready Tools: Includes lifecycle assessment tools, emission-tracking AI, and smart energy-management systems.
Efficiency & Responsibility: Boost operational efficiency, reduce waste, and accelerate your organization’s journey towards a cleaner, climate-conscious future.

Akční cena

140 EUR

169 EUR including VAT

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Course dates
Course description
Target group
Course structure
Prerequisites
Certification

Course dates

Starting date: Upon request

Type: Self-paced

Course duration: 8 hours

Language: en

Price without VAT: 140 EUR Akční cena

	Starting date	Type	Course duration	Language		Price without VAT
	Upon request	Self-paced	8 hours	en	A	140 EUR	Register
G	Guaranteed course
A	Akční cena

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Course description

Sustainable Innovation:

AI enables the development of eco-friendly technologies, addressing environmental challenges with advanced solutions.

Resource Efficiency:

AI helps optimize energy consumption, waste management, and resource distribution, minimizing environmental impact.

Data-Driven Insights:

AI analyzes large datasets to inform sustainability strategies, providing accurate predictions and actionable solutions.

Green Business Practices:

With AI knowledge, businesses can adopt environmentally conscious practices, meeting the demand for sustainability.

Preparation for Future Challenges:

AI expertise equips professionals to lead in tackling the growing environmental issues and sustainability needs.

TensorFlow
PyTorch
Python
Climate Prediction
AI-Driven Energy Management Systems
AI-Based Resource Optimization Tools
Machine Learning for Waste Reduction
Smart Grid Optimization Software
Environmental Data Visualization Platforms
Sustainability Analytics Frameworks
AI for Biodiversity Conservation

Target group

Environmental Enthusiasts: Those passionate about leveraging technology to tackle global sustainability challenges and drive positive change.

Data Analysts: Professionals looking to apply their data analytics skills to sustainability efforts and environmental decision-making.

Tech Innovators: Individuals interested in using AI to develop innovative solutions for resource optimization and environmental conservation.

Environmental Scientists: Experts aiming to integrate AI technologies into their research and sustainability projects for greater impact.

Business Leaders: Managers seeking to implement AI-driven sustainable practices and strategies within their organizations.

Course structure

Module 1: Introduction to AI and Sustainability

1.1 Overview of Artificial Intelligence
1.2 Introduction to Sustainability
1.3 Sustainability Challenges
1.4 AI for Green
1.5 Case Study: AI Models for Climate Change Prediction
1.6 Hands On: Visualizing Global CO₂ Emissions Trends with GPT

Module 2: AI Techniques for Sustainability Solutions

2.1 Introduction to Machine Learning for Sustainability
2.2 Supervised Learning for Environmental Impact
2.3 Unsupervised Learning for Environmental Insights
2.4 Reinforcement Learning for Sustainable Systems
2.5 Green AI: Sustainable AI Models
2.6 Hands-On

Module 3: AI for Climate Change Mitigation

3.1 AI in Climate Modeling
3.2 AI for Renewable Energy Integration
3.3 Carbon Footprint Reduction
3.4 Case Study: Optimizing Wind Turbine Operations with AI
3.5 Hands-On Exercises

Module 4: AI in Sustainable Energy Systems

4.1 AI for Energy Optimization
4.2 Renewable Energy Integration
4.3 AI in Energy Storage and Efficiency
4.4 Case Study: AI-Powered Smart Grids: Optimizing Energy Distribution and Integrating Renewables
4.5 Hands-On Exercises: Optimizing Smart Grid Load Balancing

Module 5: AI for Sustainable Agriculture

5.1 Precision Agriculture and Resource Optimization
5.2 AI for Pest and Disease Detection
5.3 Sustainable Farming and Decision Support Systems
5.4 Case Study: AI in Precision Agriculture
5.5 Hands-On: Predicting Crop Yields with Machine Learning

Module 6: AI in Waste Management and Circular Economy

6.1 AI for Waste Sorting and Recycling
6.2 AI for Waste-to-Energy Solutions
6.3 Circular Economy and Resource Recovery
6.4 Case Study: AI for Waste Sorting and Recycling
6.5 Hands-On: Building a Waste Sorting Classifier with AI

Module 7: AI for Biodiversity Conservation and Environmental Monitoring

7.1 AI in Remote Sensing for Environmental Monitoring
7.2 Wildlife Tracking and Conservation
7.3 AI for Ecosystem Health Monitoring
7.4 Case Study: AI for Deforestation Monitoring
7.5 Hands-On: Detecting Deforestation Using Satellite Imagery

Module 8: AI for Water Resource Management

8.1 AI for Water Consumption Prediction
8.2 AI for Smart Irrigation Systems
8.3 Water Quality Monitoring and Analysis
8.4 Case Study: AI for Smart Irrigation Systems
8.5 Hands-On: Optimizing Irrigation Systems with AI

Module 9: AI for Sustainable Cities and Smart Urban Development

9.1 AI in Smart City Infrastructure
9.2 Sustainable Mobility and Transportation
9.3 AI in Urban Resource Optimization
9.4 Case Study: AI for Urban Air Quality Monitoring
9.5 Hands-On: Optimizing Traffic Flow and Reducing Emissions with AI-Driven Smart Traffic Management

Prerequisites

Basic Knowledge of Artificial Intelligence, Understanding of Sustainability Issues, Data Analytics Skills, Familiarity with Environmental Science, Programming Skills