AI/ML Engineering Training Course
Build expertise in Artificial Intelligence and Machine Learning through comprehensive, hands-on training in Python, Machine Learning, Deep Learning, Large Language Models (LLMs), MLOps, TensorFlow, and PyTorch. Design, develop, deploy, and optimize production-ready AI solutions using industry-standard tools, modern engineering practices, and real-world projects.
Course Objectives:
- Understand the complete AI/ML lifecycle from data preparation to production deployment.
- Build and evaluate Machine Learning and Large Language Model (LLM) applications using industry-standard tools.
- Develop practical skills in Python, Machine Learning, Deep Learning, and MLOps.
- Deploy, monitor, and optimize AI/ML models for production environments.
- Build production-ready AI solutions through hands-on, real-world projects.
Course Content:
Module 1: Python and Data Foundations
- Python programming for AI/ML
- Git version control
- Linux/Command Line fundamentals
Module 2: Data Handling
- Data cleaning and preprocessing
- Data transformation with Pandas & Polars
- Data pipelines and preparation
Module 3: Core Machine Learning
- Regression and classification
- Model training and evaluation
- Decision Trees, Random Forest, XGBoost & LightGBM
Module 4: Deep Learning with PyTorch
- PyTorch fundamentals
- Neural network development
- Model training and evaluation
Module 5: Working with Hugging Face & Pretrained Models
- Hugging Face ecosystem
- Loading pretrained models
- Fine-tuning transformer models
- Model inference and deployment
Module 6: Building LLM Applications with RAG
- Large Language Models (LLMs)
- LangChain framework
- Retrieval-Augmented Generation (RAG)
- Vector databases
Module 7: Building AI Agents
- LangGraph fundamentals
- AI agent architecture
- Tool calling and workflow automation
- Multi-step reasoning systems
Module 8: Evaluating Models and Prompts
- AI model evaluation
- Prompt engineering and comparison
- Output quality and accuracy testing
- Error detection and performance validation
Module 9: MLOps: Tracking, Serving & Deploying Models
- MLflow experiment tracking
- Model serving with APIs
- Model deployment
- Production-ready AI systems
Module 10: Monitoring Models
- Model performance monitoring
- Data drift detection
- Model maintenance and retraining
- Production monitoring strategies
Module 11: Capstone Project
- End-to-end AI/ML project
- Dataset preparation
- Machine Learning model or LLM application development
- Deployment and monitoring of a production-ready AI solution
Course Prerequisites:
- Basic computer proficiency
- A laptop capable of running Python and AI/ML development tools
- No prior AI/ML experience required. Basic programming knowledge is beneficial, but Python fundamentals are covered from the beginning in Module 1.
Learning Outcomes:
- Use Python and Git confidently for AI/ML work
- Clean and prepare data for machine learning
- Build, train, and evaluate traditional machine learning models
- Build and train deep learning models using PyTorch
- Use pretrained models from Hugging Face instead of training from scratch
- Build an LLM application using retrieval-augmented generation (RAG)
- Build a simple AI agent that can plan and complete multi-step tasks
- Evaluate model and prompt quality before release
- Track, serve, and deploy models using MLOps tools
- Monitor a deployed model and respond to drift or performance issues
International Student Fees: 1599 USD
Flexible Class Options
- Corporate Group Training | Fast-Track
- Weekend Classes For Professionals SAT | SUN
- Online Classes-Live Virtual Class(L.V.C) Online Training
