Climate change is currently the world’s biggest challenge and slowing it down is an extremely urgent matter. If we fail to take control of this situation, the world will have a bigger problem at its hands than Covid-19. For this, every possible solution is required, which also includes making use of machine learning!
Yes, machine learning can help us combat the problem of climate change. According to experts, machine learning can be implemented in 13 critical areas including energy production, education, finance, solar geoengineering, CO2 removal, etc.
Let us discuss in detail how machine learning can help us combat climate change –
Better Estimation & Climate Predictions
Machine learning is a good fit for data-rich projects, providing datasets of depth & variety. Machine learning can add new, improved insights to the climate informatics that help with predicting extreme events such as hurricanes, reconstruction of past climate conditions, downscaling, etc. Machine learning algorithms are used for better predictions that can help climate officials make informed decisions. This will further help the governments to make better preparations for what’s coming.
Better Tracking & Monitoring
Machine learning certification effectively streamlines business workflows and enhances legacy systems. Visual climate change analytics get an upper hand with computer vision technologies like object recognition. The applications help track and monitor data – from the scale of deforestation to generating data for solar panels, to collecting information regarding updated climate policies across the globe.
Improved Energy Efficiency
Artificial intelligence can effectively improve power efficiency by 15% in the coming years. Machine learning can help the officials with power generation as well as its distribution. Be it is autonomous maintenance, leak monitoring to optimizing route & managing fleets. Further, the technology can read through data and forecast energy generation & demand to help the suppliers utilize the resources better.
Tracking the Carbon
The analysis of the images of power plants is automated with AI to receive regular updates regarding emissions. AI also helps crunch the numbers of nearby infrastructures and their electricity usage, to measure the plant’s impact. This comes in handy for gas-powered plants or coal-powered plants.
AI is deployed by organizations as it helps in avoiding waste. Whether it’s reducing energy waste from the buildings or understanding the demand & supply of carbon emission, AI reduces wastages in terms of time, money, and material.
AI and machine learning have a higher success rate to bring the change in climate conditions.
These technologies can help researchers and climate officials to test new theories and come up with better solutions as well as climate-friendly innovative ideas.Climate change is a major challenge the world is facing today and the faster people get familiar with AI & machine learning concepts, the better solutions we will be able to come up with
Machine Learning – Reshaping the World
In the coming years, machine learning will become a crucial part of the world. The demand for machine learning will boom significantly and its tools will surely help fight catastrophic climate change.
Upskill Yourself with Cognixia’s Machine Learning Training!
Cognixia – the world’s leading digital talent transformation company offers a thorough, comprehensive machine learning course with Python that can help you understand the implementation of machine learning along with details about how to use the Python programming language for machine learning which would, in turn, allow you to leverage that knowledge to build a successful career in machine learning.
Our machine learning certification course covers all the important concepts, libraries, and techniques that would help the learners accelerate their careers in machine learning.
Here’s what you will learn with this machine learning training course –
- Introduction to Python Programming, its various packages, & the related functions
- Data wrangling via Python
- Introduction to machine learning and Python
- Regression and classification with supervised learning
- Dimensionality Reduction
- Clustering with unsupervised Learning
- Performance evaluation & model selection
- Recommendation engines
- Association rules mining
- Time series analysis
- Reinforcement learning
- Introduction to deep learning
- Artificial neural networks
