Gen AI

Generative AI & Machine Learning Engineer

Become a professional Generative AI & Machine Learning Engineer

GEN AI Features

Generative AI (gen AI) is a powerful subset of artificial intelligence designed to create new content such as text, images, videos, code, and music, often rivaling human creativity and productivity. Its features and strengths lie in its adaptability, automation, creativity, personalization, and potential to radically transform industries and boost global productivity.

Course Pre-Requisites

Basic computer knowledge is required for this course.

Creativity & Novelty

Gen AI can produce highly creative and original content—including stories, images, music, code, and art—pushing the boundaries of human imagination by learning from patterns in vast datasets

Data Efficiency & Adaptability

It can generate meaningful outputs from minimal data, adapting its knowledge and functionality to new domains with only minor adjustments

Automation

Gen AI excels at automating complex, repetitive, or manual tasks, available 24/7 for productivity without fatigue

CURRICULLUM – MODULES

Introduction to Data Analytics & Visualization

Module 1
  • Introduction to Data Wrangling
  • Essential NumPy
  • Data Manipulation, Cleaning & Transformation using Pandas
  • Data Visualization using Seaborn

Data Analytics Foundations

Module 2
  • Python for data analysis review
  • NumPy fundamentals
  • Statistical thinking
  • Basic data structures for analysis
  • Business metrics introductio

Pandas & Data Manipulation

Module 3
  • Advanced DataFrame operations
  • Data cleaning strategies
  • Missing data handling
  • Data validation techniques
  • Performance optimization

Exploratory Data Analytics & Advanced Data Visualization

Module 4
  • Statistical measures
  • Distribution analysis
  • Correlation studies
  • Outlier detection
  • Initial data insights
  • 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, Advanced Analytics, Reporting & Automation

Module 6
  • Time series components
  • Seasonal decomposition
  • Trend analysis
  • Forecasting methods
  • Financial analysis
  • 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 & Advanced Feature Engineering

Module 7
  • ML ecosystem overview
  • Supervised vs Unsupervised learning
  • Scikit-learn fundamentals
  • Model evaluation metrics
  • ML workflow and pipelines
  • Feature transformation
  • Feature scaling methods
  • Categorical encoding techniques
  • Automated feature selection
  • Dimensionality reduction advanced

Supervised Learning: Classification & Regression

Module 8
  • Logistic regression deep-dive
  • Decision trees and Random forests
  • Support Vector Machines
  • Gradient Boosting methods
  • Multi-class classification strategies
  • Linear regression advanced concepts
  • Polynomial regression
  • Regularization techniques
  • Tree-based regression models
  • Time series regression

Ensemble Methods

Module 9
  • Bagging and boosting
  • Random forest optimization
  • XGBoost mastery
  • LightGBM and CatBoost
  • Stacking and blending

Unsupervised Learning

Module 10
  • Advanced clustering algorithms
  • Hierarchical clustering
  • Density-based clustering
  • Anomaly detection
  • Association rule learning

Deep Learning Fundamentals

Module 11
  • Neural network architecture
  • TensorFlow and Keras
  • CNNs and RNNs basics
  • Transfer learning
  • Model optimization

Model Deployment & MLOps

Module 12
  • Model serialization
  • API development (Flask/FastAPI)
  • Docker containers for ML
  • Model monitoring
  • CI/CD for ML projects

Advanced ML Topics

Module 13
  • Natural Language Processing basics
  • Computer Vision introduction
  • Recommendation systems
  • Time series forecasting
  • Reinforcement learning introduction

Industry Applications & Best Practices

Module 14
  • Model interpretability techniques
  • Ethical AI and bias detection
  • A/B testing for ML
  • Production system design
  • Industry case studies

GenAI Foundations

Module 15
  • Architecture of transformer models
  • Attention mechanisms deep-dive
  • LLM fundamentals
  • Tokenization and embeddings
  • Prompt engineering fundamentals

LLM Development & Fine-tuning

Module 16
  • Working with Hugging Face transformers
  • Model fine-tuning techniques
  • PEFT and LoRA adapters
  • Quantization methods
  • Model evaluation metrics

RAG (Retrieval Augmented Generation)

Module 17
  • Vector databases (Pinecone, Weaviate)
  • Embedding techniques
  • Chunking strategies
  • Context optimization
  • Knowledge base creation

LLM Application Development

Module 18
  • LangChain & LlamaIndex
  • Agents and tools development
  • Chain of thought prompting
  • Output parsing and validation
  • Error handling in LLM apps

Multimodal Applications

Module 19
  • Stable Diffusion implementation
  • Image generation and manipulation
  • Text-to-image systems
  • Image-to-text applications

GenAI Infrastructure

Module 20
  • Model deployment strategies
  • Scalability considerations
  • Caching mechanisms
  • Cost optimization
  • Performance monitoring

Advanced Topics

Module 21
  • Few-shot and zero-shot learning
  • Constitutional AI
  • Model alignment techniques
  • Custom model development
  • Hybrid architectures

Production Development

Module 22
  • Testing GenAI applications
  • CI/CD for LLM applications
  • Version control for models
  • A/B testing strategies
  • Monitoring and logging

Industry Applications

Module 23
  • Document processing systems
  • Chatbot development
  • Content generation tools
  • Code generation applications
  • Virtual assistants

Tools Covered

Course Includes

LIVE Interactive Sessions

Quizzes, Assignments & Projects

Study Materials & Recordings

Certificate

Skills You Will Learn

  • Develop AI applications using LangChain and LlamaIndex - create conversational agents, implement RAG (Retrieval Augmented Generation), and build knowledge-based QA systems with vector databases:
  • Develop AI applications using LangChain and LlamaIndex - create conversational agents, implement RAG (Retrieval Augmented Generation), and build knowledge-based QA systems with vector databases:
  • Create image generation applications - implement text-to-image models using Stable Diffusion, build image editing applications, and develop custom image generation pipelines with control nets:
  • Design AI safety and evaluation frameworks - implement content filtering, develop prompt injection defenses, create evaluation metrics, and ensure responsible AI deployment with proper guardrails:
  • Deploy and scale GenAI applications - build production-ready applications using FastAPI/Flask, implement efficient caching strategies, optimize inference pipelines, and manage costs in cloud environments: