29 Sections
77 Lessons
20 Weeks

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Introduction to Machine Learning
0
Supervised Learning
1
- 2.1
  Learn from labeled data (regression, classification)
Unsupervised Learning
1
- 3.1
  Discover hidden patterns (clustering, dimensionality reduction)
Reinforcement Learning
1
- 4.1
  Learn through rewards and feedback loops
Neural Networks
0
ANN (Artificial Neural Network)
3
- 6.1
  Basic feedforward network
- 6.2
  Used for classification and regression
- 6.3
  Learns non-linear patterns
GenAI System Design
4
- 7.1
  Advanced Prompt Engineering and GenAI System
- 7.2
  Design Prompting Multimodal Models LLM
- 7.3
  Frameworks such as LangChain and LLaMa Index LLM
- 7.4
  Evaluation Methods Data Security and Governance AI Ethics
Advanced Generative AI
7
- 8.1
  Information Retrieval Principles
- 8.2
  Embeddings and Vector Databases
- 8.3
  RAG Architectures
- 8.4
  Advanced Multimodal GenAI Models
- 8.5
  Agentic Systems and Multi-Agent Systems
- 8.6
  LLM Deployment
- 8.7
  GenAI Optimisations
LangChain
3
- 9.1
  Custom chatbot and agent development
- 9.2
  Integration with APIs and databases
- 9.3
  Dynamic automation workflows
LangGraph
3
- 10.1
  Support for looping and branching workflows
- 10.2
  Multi-agent collaboration and orchestration
- 10.3
  Seamless extension of LangChain capabilities
Hugging Face
3
- 11.1
  Pre-trained models for NLP, vision, and speech
- 11.2
  Transformers library for deep learning
- 11.3
  Community-driven model hub and collaboration
N8N
3
- 12.1
  Visual workflow builder
- 12.2
  Integration with APIs and databases
- 12.3
  Flexible automation with minimal coding
MAKE
3
- 13.1
  Drag-and-drop workflow creation
- 13.2
  Seamless app integrations
- 13.3
  Enterprise-ready automation solutions
Streamlit App Development
0
Streamlit Fundamentals
5
- 15.1
  Introduction to Streamlit Architecture
- 15.2
  App Structure & Layout Design
- 15.3
  Widgets: sliders, buttons, inputs, file uploads
- 15.4
  Displaying DataFrames, Charts, and Metrics
- 15.5
  Handling User Inputs & State Management
Data & Model Integration
5
- 16.1
  Integrating Pandas, NumPy & Visualization Libraries
- 16.2
  Connecting ML Models (Scikit-learn, TensorFlow, PyTorch)
- 16.3
  Real-time Predictions & Interactive Controls
- 16.4
  Uploading & Processing External Data
- 16.5
  Performance Optimization Techniques
Deployment & Scaling
5
- 17.1
  App Configuration & Secrets Management
- 17.2
  Streamlit Cloud & Local Deployment
- 17.3
  API Integration
- 17.4
  Authentication Basics
- 17.5
  Sharing & Hosting Data Apps
Projects
4
- 18.1
  Interactive Data Dashboard
- 18.2
  ML Prediction Web App
- 18.3
  AI-Powered Analytics Tool
- 18.4
  Real-Time Data Monitoring Application
Flask Web Development
0
Key Business Questions
3
- 20.1
  How can we build secure and scalable backend web applications using Flask?
- 20.2
  How can we integrate databases into web applications efficiently?
- 20.3
  How can we develop REST APIs for real-world business systems?
Flask Fundamentals
5
- 21.1
  Introduction to Flask & WSGI Concept
- 21.2
  Application Structure & Project Setup
- 21.3
  Routing & HTTP Methods (GET, POST)
- 21.4
  Working with Templates (Jinja2)
- 21.5
  Passing Data to Views
Database & Authentication
5
- 22.1
  Models & ORM Basics
- 22.2
  Flask-SQLAlchemy Integration
- 22.3
  CRUD Operations
- 22.4
  User Registration & Login System
- 22.5
  Session Management & Access Control
REST API & Deployment
5
- 23.1
  Building RESTful APIs
- 23.2
  JSON Responses & API Testing
- 23.3
  Error Handling
- 23.4
  Environment Configuration
- 23.5
  Deploying Flask Applications
Projects
4
- 24.1
  Blog Management System
- 24.2
  Task Management App (CRUD + Login)
- 24.3
  REST API for Product Management
- 24.4
  Authentication-Based Web Application
Capstone Projects
0
Feature Engineering & Model Selection
1
- 26.1
  Predict fraudulent insurance claims using the Mendeley farmers insurance claims dataset or network intrusion events using historical network activity data
Semantic Classiﬁcation
1
- 27.1
  It powers applications like Fake News Detection and Job Role Classification by accurately analyzing content beyond keywords, enabling smarter NLP systems for media, hiring, and moderation.
GenAI System Design
1
- 28.1
  Analyse Amazon customer reviews to identify prevalent sentiments & to improve product offerings & enhance customer satisfaction or customer feedback to derive actionable insights for business
RAG
1
- 29.1
  Develop an RAG system to transform Long Beach County Municipal meetings transcripts into actionable insights for decision making

Agentic AI & AI Automation Course

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