AI-Integrated Electric Vehicle Development Engineer
Become a professional AI-Integrated Electric Vehicle Development Engineer
AI Integrated Electric Vehicle Features
AI-integrated electric vehicles come with smart navigation that learns routes and reduces travel time. They use intelligent battery management to extend driving range and efficiency. Safety is enhanced with AI-powered sensors for collision avoidance and driver assistance. These vehicles also support self-driving features, making travel more convenient and futuristic.
Course Pre-Requisites
Basic computer knowledge is required for this course.
Creativity & Novelty
AI-integrated electric vehicles bring creativity by merging clean energy with smart, adaptive systems. Their novelty lies in self-learning features that enhance safety, efficiency, and sustainability.
Data Efficiency & Adaptability
AI-integrated electric vehicles use data efficiency to optimize battery usage, navigation, and performance. Their adaptability allows them to learn driving patterns and adjust to different road and traffic conditions.
Automation
AI-integrated electric vehicles use automation for self-driving, smart navigation, and optimized battery management. This reduces human effort, enhances safety, and ensures efficient performance.
CURRICULLUM – MODULES
Data Analytics Foundations
Module 1
- Python for data analysis review
- NumPy fundamentals
- Statistical thinking
- Basic data structures for analysis
- Business metrics introduction
Pandas & Data Manipulation
Module 2
- Advanced DataFrame operations
- Data cleaning strategies
- Missing data handling
- Data validation techniques
- Performance optimization
Exploratory Data Analytics
Module 3
- Statistical measures
- Distribution analysis
- Correlation studies
- Outlier detection
- Initial data insights
Advanced Data Visualization
Module 4
- Matplotlib mastery
- Interactive Plotly dashboards
- Seaborn for statistical visualization
- Dashboard design principles
- Storytelling with data
Business Statistics
Module 5
- Hypothesis testing
- A/B testing
- Statistical significance
- Regression analysis
- Business metrics & KPIs
Time Series Analysis
Module 6
- Time series components
- Seasonal decomposition
- Trend analysis
- Forecasting methods
- Financial analysis
Advanced Analytics & Reporting Automation
Module 7
- Segmentation techniques
- Clustering analysis
- Dimensionality reduction
- Feature engineering
- Predictive analytics basics
- Automated reporting
- Data pipelines
- ETL processes
- Scheduling and monitoring
- Quality assurance
Machine Learning Foundations
Module 8
- ML ecosystem overview
- Supervised vs Unsupervised learning
- Scikit-learn fundamentals
- Model evaluation metrics
- ML workflow and pipelines
Advanced Feature Engineering
Module 9
- Feature transformation
- Feature scaling methods
- Categorical encoding techniques
- Automated feature selection
- Dimensionality reduction advanced
Supervised Learning: Classification
Module 10
- Logistic regression deep-dive
- Decision trees and Random forests
- Support Vector Machines
- Gradient Boosting methods
- Multi-class classification strategies
Supervised Learning: Regression
Module 11
- Linear regression advanced concepts
- Polynomial regression
- Regularization techniques
- Tree-based regression models
- Time series regression
Ensemble Methods
Module 12
- Bagging and boosting
- Random forest optimization
- XGBoost mastery
- LightGBM and CatBoost
- Stacking and blending
Unsupervised Learning
Module 13
- Advanced clustering algorithms
- Hierarchical clustering
- Density-based clustering
- Anomaly detection
- Association rule learning
Deep Learning Fundamentals
Module 14
- Neural network architecture
- TensorFlow and Keras
- CNNs and RNNs basics
- Transfer learning
- Model optimization
Model Deployment & MLOps
Module 15
- Model serialization
- API development (Flask/FastAPI)
- Docker containers for ML
- Model monitoring
- CI/CD for ML projects
GenAI Foundations
Module 16
- Architecture of transformer models
- Attention mechanisms deep-dive
- LLM fundamentals
- Tokenization and embeddings
- Prompt engineering fundamentals
LLM Development & Fine-tuning
Module 17
- Working with Hugging Face transformers
- Model fine-tuning techniques
- PEFT and LoRA adapters
- Quantization methods
- Model evaluation metrics
RAG (Retrieval Augmented Generation)
Module 18
- Vector databases (Pinecone, Weaviate)
- Embedding techniques
- Chunking strategies
- Context optimization
- Knowledge base creation
LLM Application Development
Module 19
- LangChain & LlamaIndex
- Agents and tools development
- Chain of thought prompting
- Output parsing and validation
- Error handling in LLM apps
Multimodal Applications
Module 20
- Stable Diffusion implementation
- Image generation and manipulation
- Text-to-image systems
- Image-to-text applications
GenAI Infrastructure
Module 21
- Model deployment strategies
- Scalability considerations
- Caching mechanisms
- Cost optimization
- Performance monitoring
Advanced Topics
Module 22
- Few-shot and zero-shot learning
- Constitutional AI
- Model alignment techniques
- Custom model development
- Hybrid architectures
AI Fundamentals for Automotive Applications
Module 23
- Machine learning algorithms for automotive use cases
- Deep learning frameworks (TensorFlow, PyTorch) for vehicle systems
- Edge computing and real-time AI processing in vehicles
- Data preprocessing and feature engineering for vehicle data
- Model deployment strategies for automotive environments
- AI hardware selection (GPUs, TPUs, automotive chips)
EV Data Architecture and IoT Integration
Module 24
- Vehicle data collection systems and sensor integration
- Cloud-based data lakes for EV fleet management
- Real-time data streaming and processing pipelines
- Data security and privacy in connected vehicles
- API development for vehicle-to-cloud communication
- Data visualization dashboards for EV operations
Predictive Maintenance AI Systems
Module 25
- Battery health prediction using machine learning
- Motor and inverter failure prediction algorithms
- Anomaly detection for EV component monitoring
- Maintenance scheduling optimization algorithms
- Digital twin development for predictive analytics
- ROI analysis and maintenance cost optimization
Smart Charging AI and Grid Integration
Module 26
- Dynamic pricing optimization for EV charging
- Load balancing algorithms for charging networks
- Demand forecasting using time-series analysis
- Vehicle-to-grid (V2G) optimization with reinforcement learning
- Peak shaving and energy arbitrage strategies
- Integration with renewable energy sources
Autonomous Driving Systems for EVs
Module 27
- Computer vision for object detection and classification
- Sensor fusion (LiDAR, camera, radar) algorithms
- Path planning and trajectory optimization
- Reinforcement learning for driving decision-making
- Safety validation and testing frameworks
- Integration with EV powertrain control systems
Fleet Management AI Platform Development
Module 28
- Route optimization using genetic algorithms
- Driver behavior analysis and coaching systems
- Energy consumption optimization across fleets
- Predictive fleet maintenance scheduling
- Dynamic fleet rebalancing for ride-sharing
- Performance analytics and KPI dashboard development
AI-Powered Battery Management Systems
Module 29
- State of health (SOH) prediction using neural networks
- Optimal charging strategies with deep reinforcement learning
- Thermal management AI for battery longevity
- Cell balancing optimization algorithms
- Battery degradation modeling and prediction
- Second-life battery applications using AI
Natural Language Processing for Vehicle Interfaces
Module 30
- Voice command systems for EV controls
- Conversational AI for vehicle assistance
- Multilingual support and accent recognition
- Intent recognition for vehicle functions
- Integration with smart home and IoT devices
- Privacy-preserving speech processing
AI Model Development and MLOps for Automotive
Module 32
- Automotive-grade AI model training and validation
- Continuous integration/deployment (CI/CD) for AI models
- A/B testing frameworks for vehicle AI systems
- Model versioning and rollback strategies
- Over-the-air (OTA) AI model updates
- Performance monitoring and model drift detection
AI Ethics and Safety in Autonomous EVs
Module 33
- Ethical decision-making algorithms for autonomous vehicles
- Bias detection and mitigation in AI systems
- Explainable AI for automotive applications
- Safety validation using simulation and formal methods
- Regulatory compliance for AI in vehicles (ISO 26262)
- Cybersecurity for AI-enabled vehicle systems
Industry Capstone Project and Deployment
Module 34
- End-to-end AI-EV system design and implementation
- Integration with existing automotive software stacks
- Real-world testing and validation methodologies
- Commercialization strategies for AI-EV technologies
- Industry partnerships and technology licensing
- Career development in AI-automotive convergence
Course Includes
LIVE Interactive Sessions
Quizzes, Assignments & Projects
Study Materials & Recordings
Certificate
Skills You Will Learn
- Master Python for data analysis with NumPy and Pandas:
- Clean and preprocess datasets for machine learning:
- Build interactive dashboards with Plotly and Seaborn:
- Perform statistical analysis and hypothesis testing:
- Train ML models (regression, classification, clustering):
- Deploy AI models using Flask/FastAPI and Docker:
- Fine-tune LLMs with LoRA and Hugging Face Transformers:
- Develop RAG systems with vector databases:
- Engineer prompts for optimal LLM performance:
- Design multimodal AI apps (text-to-image, voice interfaces):
- Optimize autonomous driving with sensor fusion:
- Develop AI-powered smart charging algorithms:
- Build NLP systems for in-vehicle voice assistants:
- Validate AI models for ISO 26262 compliance:
- Deploy scalable GenAI infrastructure:
- Apply MLOps for CI/CD in automotive AI:
- Implement predictive maintenance for EV batteries:
