Global Academic Alliance

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DSTC-01590 Online (e-LMS) Graduate / Intermediate

Machine Learning for Battery Lifetime and Degradation Analysis

by - DSTC

Master Machine Learning for Battery Lifetime and Degradation Analysis in 4 weeks through hands-on, project-based online training with DSTC.

โ˜…โ˜…โ˜…โ˜…โ˜… Be the first to review โ€ข 3 Days ยท 4.5 hrs โ€ข e-Certificate Included
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From โ‚น2,500 + GST

๐Ÿ’ป Software & Environment Setup

AI & Machine Learning in Healthcare

The computational toolchain and environment used throughout Machine Learning for Battery Lifetime and Degradation Analysis.

Machine learning battery for PhD researchersMachine learning battery workshop 2025Battery workshopMachine learning battery hands-on trainingReliability training for researchersReliability workshop
# Recommended conda environment
conda create -n dstc_env python=3.11 -y
conda activate dstc_env
pip install torch torchvision scikit-learn monai pydicom
PyTorch / TensorFlow
Deep-learning model training
MONAI
Medical-imaging deep-learning framework
scikit-learn
Classical ML, evaluation & pipelines
Weights & Biases
Experiment tracking & reproducibility
Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.
Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.

Earn government-registered certification in Machine Learning for Battery Lifetime and Degradation Analysis

e-Certificate and e-Marksheet issued on successful completion.

View full course โ†’

Scholar Registration

For scholars whose department, college or employer pays the fee. We raise a proforma invoice to your institution; you attach the signed processing letter or bank slip.

The proforma invoice is emailed here as well as to you.
๐Ÿ“„ Upload Sponsorship Slip / Letter

Signed letter on official letterhead, or the bank transfer slip. PDF/JPG/PNG, up to 5 MB.

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