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AI, Machine Learning & Agentic AI Engineering
Join AI, Machine Learning & Agentic AI Engineering by MAB - a practical training program for focused digital growth.
Led by experienced trainers, master AI workflows, practical implementation, freelancing direction, and modern digital systems. Gain expert insights to transform your career.
Sign up now and start building job-ready skills.
AI, Machine Learning & Agentic AI Engineering: Training Content, Bonuses & Career Direction
Whats New:-
- AI Workflows
- Automation Systems
- Client Projects
- Portfolio Direction
Course Outline:-
Supervised and unsupervised learning algorithms:
ML & AI Foundations: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Feature engineering and data preparation:
ML & AI Foundations: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Model evaluation, bias, and overfitting:
ML & AI Foundations: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Python ML stack: NumPy, Pandas, scikit-learn:
ML & AI Foundations: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Feedforward and convolutional architectures:
Neural Networks & Deep Learning: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Training loops, optimizers, and regularization:
Neural Networks & Deep Learning: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Transfer learning with pre-trained models:
Neural Networks & Deep Learning: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
PyTorch fundamentals and model checkpointing:
Neural Networks & Deep Learning: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Transformer architecture and attention mechanisms:
Large Language Models: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Prompt engineering and context management:
Large Language Models: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
RAG (Retrieval-Augmented Generation) systems:
Large Language Models: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Fine-tuning with LoRA and PEFT techniques:
Large Language Models: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Agent architectures: ReAct, Plan-and-Execute, and memory:
Agentic AI Systems: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
LangChain chains, tools, and agent loops:
Agentic AI Systems: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Multi-agent orchestration with AutoGen:
Agentic AI Systems: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Tool calling, function execution, and state management:
Agentic AI Systems: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
OpenAI, Anthropic, and Gemini API integration:
AI API Integration & Orchestration: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Building AI-powered microservices:
AI API Integration & Orchestration: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Streaming responses and real-time AI outputs:
AI API Integration & Orchestration: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Containerizing AI services with Docker:
Production Deployment & MLOps: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Model serving with FastAPI and cloud endpoints:
Production Deployment & MLOps: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Monitoring model drift, latency, and costs:
Production Deployment & MLOps: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
CI/CD pipelines for AI applications:
Production Deployment & MLOps: practical training, examples, and implementation tasks for AI, Machine Learning & Agentic AI Engineering.
Portfolio and Client-Ready Execution:
Build practical assets you can show to clients, employers, or partners.
Tools, Systems, and Automation:
Use modern software, AI workflows, and repeatable systems to save time and improve delivery quality.
Growth and Monetization:
Learn how to turn your new skills into services, products, campaigns, or income opportunities.
Get Enrolled Now
Learn the core principles of AI, Machine Learning & Agentic AI Engineering. Build standout projects, implement practical systems, and gain the skills necessary to move forward in the digital landscape.
AI
Rs. 60,000
Important Note
- No registration fee
- One-time fee only
- Live lectures
- Recorded lectures
- Lifetime learning access
- No monthly subscription
Course Access
- Student portal access
- Web, iOS, and Android access
- Private learning community
- Course updates
Training Content
- Module 1: ML & AI Foundations
- Module 2: Neural Networks & Deep Learning
- Module 3: Large Language Models
- Module 4: Agentic AI Systems
- Module 5: AI API Integration & Orchestration
- Module 6: Production Deployment & MLOps
Support
- Live support
- Community help
- Q&A guidance
- Practical feedback
Resources
- Templates
- Checklists
- Tools list
- Project resources
Bonus
- Freelancing guidance
- AI workflow pack
- Portfolio direction
Our Success Stories
Meet learners who turned training into real progress. Each story is a reminder that focused learning, practice, and consistency can open strong career opportunities.
MAB Student
Earned: $3,000+
Source: Freelancing
This training gave me a clear path, practical assignments, and confidence to start offering digital services.
Frequently Asked Questions
Got questions? Find quick answers about courses, enrollment, and support.