📄 Download Student Guide

🎓 Jeeves Campus

Glass Wall View: AI/ML Data Transformation Learning

💡 Download the complete guide (PDF) using the button above →

Start Learning →
🎯

Student Dashboard START HERE

Your complete learning platform with interactive courses, real-time visualization, and progress tracking.

📚 Available Courses:
  • Python for Data Science (12 topics)
  • Machine Learning Fundamentals (15 topics)
  • H2O3 AutoML Workshop (8 topics)
  • Real-time Pipeline Monitoring (6 topics)
✨ Features:
  • Live code editor with instant feedback
  • Glass Wall pipeline visualization
  • Achievement system & progress tracking
  • Hint system for every exercise
Open Dashboard →
📓

JupyterHub Notebooks

Your personal Python workspace for coding, experimentation, and project development.

🔧 Pre-configured Environment:
  • Python 3.10 with all ML libraries
  • H2O3 connection ready to use
  • Template notebooks for quick start
  • Personal storage: /home/[username]/
📝 Example Connection:
import h2o
# Connect to H2O3 cluster
h2o.init(url='http://campus_h2o3:54321')
print("✓ Connected!")
Open JupyterHub →
🌊

H2O3 Flow

Visual machine learning interface - build models without writing code!

🎨 Visual ML Capabilities:
  • Drag-and-drop data loading
  • AutoML - automatic model selection
  • Compare 10+ algorithms instantly
  • Interactive performance charts
🚀 Quick Start:
  • Click "Import Files" to upload data
  • Select "Build Model" → Choose algorithm
  • Hit "Train" and watch it work!
  • View results in real-time
Open H2O3 →
🤖

H2OGPT Assistant

Your 24/7 AI coding assistant - ask anything about ML, Python, or debugging.

💬 Example Questions:
  • "How do I handle missing values in pandas?"
  • "Explain gradient boosting in simple terms"
  • "I'm getting ImportError, what's wrong?"
  • "What's the difference between GBM and RF?"
🎯 Best Used For:
  • Understanding error messages
  • Learning ML concepts
  • Getting code suggestions
  • Best practices & tips
Chat with AI →
⚙️

Glasswall API

Backend API for pipeline monitoring and advanced integrations.

🔌 API Endpoints:
  • GET /health - System health check
  • GET /pipeline/status - Current pipeline state
  • POST /events - Send custom events
  • GET /metrics - Performance metrics
📡 Usage Example:
import requests
api = "http://campus_glasswall_api:8080"
status = requests.get(f"{api}/health")
print(status.json())
API Docs →
👁️

Glass Wall View

Watch your ML pipelines execute in real-time - see inside the "black box"!

🔍 5-Stage Pipeline:
  • 1️⃣ Data Ingestion - Loading datasets
  • 2️⃣ Data Cleaning - Removing errors
  • 3️⃣ Feature Engineering - Creating variables
  • 4️⃣ Model Training - Building ML models
  • 5️⃣ Evaluation - Testing performance
📊 Real-time Metrics:
  • Progress bars for each stage
  • Live resource usage (CPU/Memory)
  • AI narration of what's happening
  • Performance statistics
Check Status →

📚 Quick Start Guide

  1. Download the PDF Guide: Click the red button at top-right for complete instructions
  2. Get Your Credentials: Username: jc_xxxxx | Password: Pass@2025
  3. Start at Student Dashboard: Click "Start Learning" above or go to port 32010
  4. Pick Your First Course: Choose from Python, ML, or H2O3 workshops
  5. Code in JupyterHub: Write Python code and connect to H2O3 easily
  6. Try Visual ML: Use H2O3 Flow for no-code machine learning
  7. Ask H2OGPT: Get help anytime from the AI assistant
  8. Watch Glass Wall: See your pipelines execute in real-time

🎯 Learning Objectives

Week 1: Foundations

  • ✓ Python for Data Science
  • ✓ Data Cleaning Techniques
  • ✓ Exploratory Data Analysis
  • ✓ H2O3 Basics

Week 2: ML Models

  • ✓ Feature Engineering
  • ✓ Model Training (GBM, RF, DL)
  • ✓ Model Evaluation
  • ✓ AutoML Pipeline

Week 3: Advanced

  • ✓ Hyperparameter Tuning
  • ✓ Ensemble Methods
  • ✓ Model Deployment
  • ✓ Production Pipelines

🆘 Getting Help

💡 In Exercises

Click the "Hint" button for step-by-step guidance on any exercise.

🤖 H2OGPT

Ask the AI assistant anything 24/7 at port 32002.

📖 Templates

Check tutorial notebooks in JupyterHub /templates/ folder.

📧 Instructor

Email: instructor@svsconsultingindia.in