Generative AI Developer course with hands-on Project work

Generative AI developer course with hands-on project and certification:

The demand for Generative AI Engineers is skyrocketing, with companies actively seeking professionals who can develop, fine-tune, and deploy AI-powered solutions.

If you’re looking for a career-defining AI skill set, this Generative AI developer course with hands-on projects will take you from beginner to expert in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and Cloud AI Deployments.

Large Language Models (LLMs) like ChatGPT, Copilet, Llama and Gemini are transforming industries.

Why Become a Certified Generative AI Engineer?

AI is transforming industries, and companies are seeking certified Generative AI Engineers who can develop AI-powered applications with LLMs, RAG, and AI Agents.

Earning an industry-recognized Generative AI skill badges and certification enhances your credibility and opens doors to high-paying AI roles.

Don’t miss out! Enroll in this Generative AI Developer Course with Certification today and future-proof your career.

Module A. Fundamentals Foundations of AI

Introduction to AI

• What is AI? – Narrow AI vs. General AI vs. Super AI
• AI vs. Machine Learning vs. Deep Learning vs. Generative AI
• Real-world applications of AI in different industries
• Key enterprise AI roles (AI Engineer, AI Architect, Generative AI Developer)

Module B. Deep Learning & Neural Networks

Neural Network Basics

• Understanding Perceptron, Forward & Backward Propagation
• Activation Functions: ReLU, Sigmoid, Tanh, Softmax
• Gradient Descent & Optimizers (SGD, Adam, RMSprop)

Hands-on Deep Learning Frameworks

• TensorFlow vs. PyTorch: Comparison & Basic Operations
• Working with Tensors, Training Loops, and Model Definition

Convolutional & Recurrent Neural Networks

• CNNs for image classification (MNIST, CIFAR-10)
• RNNs & LSTMs for sequence modeling & time-series forecasting

Module C. Transformer & Large Language Models (LLMs)

Transformer Architecture Deep Dive

• Self-Attention Mechanism & Multi-Head Attention
• Positional Embeddings & Why Transformers Work
• Encoder-Decoder vs. Decoder-only (GPT-like) Models

LLM Ecosystem & Model Families

• OpenAI’s GPT-3.5 & GPT-4, Google Gemini, Meta LLaMA
• Tokenization: Byte-Pair Encoding (BPE), WordPiece
• Embeddings: Word2Vec, Transformer-based embeddings

Fine-Tuning vs. Prompt Engineering

• Fine-Tuning Techniques: LoRA, Adapters, Parameter-efficient methods
• Prompt Engineering: Zero-shot, Few-shot, Chain-of-Thought, System Prompts

Module D. Python Foundations & ML Basics

Python Programming Essentials

• Python Basics: Data types (strings, lists, tuples, dicts), control flow, functions
• OOP Concepts (Optional Overview)

Advanced Python Data Structures

• List & Dictionary Comprehensions, Sets, Decorators (Optional)

Environment Setup & Data Handling

• Virtual Environments: venv, conda
• Version Control: Git & GitHub basics
• File I/O Operations: Reading/Writing CSV, JSON
• Data Handling with Pandas

Module E. Introduction to Machine Learning with Python

Machine Learning Foundations

• Overview of scikit-learn
• Train/Test Splitting for Supervised Learning
• Implementing Simple Linear & Logistic Regression

Model Evaluation Metrics

• Understanding Accuracy, Precision, Recall, and F1-score
• Visualizing Confusion Matrices

Module F. Deploying Python APIs on Azure

Azure Cloud Fundamentals

• Understanding Resource Groups & Azure Container Registry (ACR)
• Deploying APIs using Azure Web App for Containers & Azure Container Instances (ACI)

Deployment Pipeline on Azure

• Pushing & Pulling Docker Images from ACR
• Configuring Environment Variables & Application Settings

Observability & Monitoring

• Setting up Azure Monitor & Application Insights
• Logging & Performance Metrics

Module G. Deploying Python APIs & ML Models on Google Cloud Platform (GCP)

Google Cloud Platform (GCP) Overview

• Understanding Google Cloud Services for AI & ML
• Introduction to Cloud Run, GKE, and App Engine

Building & Deploying ML Models on GCP

• Saving & Loading ML Models
• Using scikit-learn pipelines (classification/regression)
• Hyperparameter tuning (Grid Search, Random Search)
• Model serialization using Joblib, Pickle, ONNX
• Best practices for reproducibility

FastAPI Integration for Model Serving

• Creating a /predict API endpoint for real-time inference
• Handling model loading in memory or on demand

Containerization & CI/CD on GCP

• Dockerizing Python-based ML models
• Submitting images to Google Container Registry (GCR)
• CI/CD with GitHub Actions & GCP Cloud Build

Module H. Retrieval-Augmented Generation (RAG) & Vector Databases

Understanding RAG Fundamentals

• How RAG reduces hallucinations in LLMs
• Data chunking, Indexing, Retrieval & Generation Pipeline

Vector Databases & Embeddings

• Working with Pinecone, Chroma, Weaviate, Milvus
• Embedding Models: OpenAI Embeddings, Sentence Transformers

Implementing RAG Pipelines

• Index creation, Query Mechanism, and Document Retrieval
• LangChain for efficient RAG-based chatbots

Module I. Prompt Engineering & Its Strategies

Understanding Prompt Engineering Strategies

• Instruction-Based Prompting
• Context-Based Prompting
• Example-Based Prompting
• Role-Based Prompting

Understand  LLM Model Key Parameters

• Temperature and its settings
• Frequency Penalty
• Presence Penalty
• Top-k
• Top-p
• Stop Sequence 

Prompt Usage Techniques

• Zero-shot Learning
• One-shot Prompt
• Few-shot Learning
• Fine-tuning

Understanding Tokenization in LLM

• Tokenization Process in LLMs
• Chunking
• Context Window

Common Challenges in LLMs

• Hallucination in LLM
• Ambiguity in Prompt Design 
• Bias and Fairness

Module J. AI Agents & Autonomous Workflows

What is an AI Agent?

• Understanding Autonomy, Multi-Step Reasoning, Planning

AI Agent Frameworks

• LangChain Agents, Semantic Kernel, Crew AI

AI Agents with APIs & Tools

• Integrating external APIs (weather, stock market, calculator)
• Multi-step decision-making with Chain-of-Thought prompting

Hands-on Lab:

• Build an AI Agent that calls a mock external API️
• Implement multi-step reasoning & decision-making

Module K. Fine-Tuning & Benchmarking Domain-Specific Models

Advanced Fine-Tuning Techniques

• Full fine-tuning vs. LoRA, Adapters, QLoRA
• Domain adaptation & dataset curation

Benchmarking & Model Performance

• Evaluation Metrics: BLEU, ROUGE, Perplexity, FID
• Scalability concerns (distributed training, multi-GPU setups)

Hands-on Lab:

• Fine-tune LLaMA/GPT-Neo on a domain-specific dataset
• Measure improvements vs. baseline models

Module L. MLOps, AIOps & Cloud Deployment

Model Deployment Strategies

• Containerization (Docker, Kubernetes, Serverless AI Models)
• REST & GraphQL APIs using FastAPI/Flask

Observability & Monitoring

• Drift detection, logging, setting up monitoring dashboards

Cloud AI Deployments (Azure, GCP)

• Azure: AI Studio
• GCP: Cloud Run, Vertex AI 

Module M. Responsible AI & Real-World Use Cases

Responsible AI Principles

• Bias, Fairness, Transparency in Generative AI
• GDPR, HIPAA Compliance & Prompt Security

Ethical AI in Business Applications

• Bias mitigation techniques in LLMs & RAG systems

Capstone Workshop

• End-to-End AI solution development
• Benchmarking domain-specific fine-tuned model