04-27-2025, 12:36 PM
Machine Learning For Electric Vehicles
![[Image: cFnAOjhFGiDw1R4EPbZfvjHdvi3YtMA6.avif]](https://sanet.pics/storage-11/0425/avif/cFnAOjhFGiDw1R4EPbZfvjHdvi3YtMA6.avif)
Published 4/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 700.05 MB | Duration: 2h 35m
A Practical Guide to Using Machine Learning in the World of Electric Vehicles
What you'll learn
Explain the fundamental components and working of electric vehicles.
Understand supervised, unsupervised, and reinforcement learning.
Predict EV battery life and optimize charging cycles.
Understand the types of data collected by EVs (e.g., GPS, battery, motor temperature).
Requirements
Basic Knowledge of Python Programming
Fundamentals of Machine Learning
Basic Understanding of Mathematics
Interest in Electric Vehicles or Automotive Technology
Description
Are you passionate about electric vehicles and curious about how machine learning is transforming the future of clean mobility?"Machine Learning for Electric Vehicles" is a hands-on course designed for engineers, data scientists, students, and professionals who want to apply machine learning techniques to solve real-world challenges in the EV industry.Whether you're aiming to predict battery life, optimize charging patterns, perform predictive maintenance, or analyze sensor data from EVs-this course gives you the tools, skills, and confidence to make it happen.What You'll Learn:The fundamentals of electric vehicle systems and their data sources.Core machine learning concepts with real-world EV use cases.How to build predictive models for EV battery health and range estimation.Practical applications like smart charging, vehicle diagnostics, and energy optimization.Techniques for working with time-series and sensor data using Python.How to evaluate, improve, and deploy ML models in EV scenarios.Who This Course Is For:Engineering or computer science students interested in electric mobility.Data scientists and ML enthusiasts looking to work on impactful projects.Automotive professionals transitioning into the EV and AI space.Researchers, clean-tech innovators, and EV startup founders.No prior experience with electric vehicles is required-just basic Python and a passion for innovation!Join now and start building the future of smart, sustainable transportation with machine learning.
Engineering Students (Electrical, Electronics, Mechanical, or Computer Science),Data Science & Machine Learning Enthusiasts,Professionals in the Automotive and EV Industry,Researchers & Academics,Startups & Entrepreneurs in Clean Tech and EV Space,Tech Hobbyists and Tinkerers,Government or NGO Professionals in Smart City or Energy Sectors
rapidgator.net:
nitroflare.com:
Published 4/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 700.05 MB | Duration: 2h 35m
A Practical Guide to Using Machine Learning in the World of Electric Vehicles
What you'll learn
Explain the fundamental components and working of electric vehicles.
Understand supervised, unsupervised, and reinforcement learning.
Predict EV battery life and optimize charging cycles.
Understand the types of data collected by EVs (e.g., GPS, battery, motor temperature).
Requirements
Basic Knowledge of Python Programming
Fundamentals of Machine Learning
Basic Understanding of Mathematics
Interest in Electric Vehicles or Automotive Technology
Description
Are you passionate about electric vehicles and curious about how machine learning is transforming the future of clean mobility?"Machine Learning for Electric Vehicles" is a hands-on course designed for engineers, data scientists, students, and professionals who want to apply machine learning techniques to solve real-world challenges in the EV industry.Whether you're aiming to predict battery life, optimize charging patterns, perform predictive maintenance, or analyze sensor data from EVs-this course gives you the tools, skills, and confidence to make it happen.What You'll Learn:The fundamentals of electric vehicle systems and their data sources.Core machine learning concepts with real-world EV use cases.How to build predictive models for EV battery health and range estimation.Practical applications like smart charging, vehicle diagnostics, and energy optimization.Techniques for working with time-series and sensor data using Python.How to evaluate, improve, and deploy ML models in EV scenarios.Who This Course Is For:Engineering or computer science students interested in electric mobility.Data scientists and ML enthusiasts looking to work on impactful projects.Automotive professionals transitioning into the EV and AI space.Researchers, clean-tech innovators, and EV startup founders.No prior experience with electric vehicles is required-just basic Python and a passion for innovation!Join now and start building the future of smart, sustainable transportation with machine learning.
Engineering Students (Electrical, Electronics, Mechanical, or Computer Science),Data Science & Machine Learning Enthusiasts,Professionals in the Automotive and EV Industry,Researchers & Academics,Startups & Entrepreneurs in Clean Tech and EV Space,Tech Hobbyists and Tinkerers,Government or NGO Professionals in Smart City or Energy Sectors
rapidgator.net:
nitroflare.com:
