AutoML_Seminar_PPT_Condensed_presentation

1. AutoML: Automating the Future of
Machine Learning
• Presented by: [Your Name]
• Reg No: [Your Reg No]
• Department of AIML, [College Name]
• Date: [Seminar Date]
• Guide: [Faculty Guide Name]

2. Introduction & What is AutoML?
• AutoML automates the process of building ML
models—data preprocessing, feature
engineering, model selection, and
hyperparameter tuning—making ML
accessible to non-experts.

3. Traditional ML vs AutoML
• Traditional ML: Manual processes, requires
expertise, time-consuming.
• AutoML: Automated, user-friendly, fast and
efficient.
• | Task | Traditional | AutoML |
• |------|-------------|--------|
• | Preprocessing | Manual | Automated |
• | Model Tuning | Grid Search | Bayesian Opt.

4. AutoML Workflow
• 1. Input data
• 2. Preprocessing
• 3. Feature Engineering
• 4. Model Selection
• 5. Hyperparameter Optimization
• 6. Evaluation & Deployment
• (Use a flowchart here)

5. Core Techniques in AutoML
• - Hyperparameter Optimization
• - Neural Architecture Search (NAS)
• - Meta-Learning
• - Ensemble Learning
• - Transfer Learning

6. Popular AutoML Tools
• - Google Cloud AutoML
• - AutoKeras
• - H2O.ai AutoML
• - TPOT
• - Auto-sklearn
• (Each supports a range of ML tasks)

7. Applications of AutoML
• - Healthcare: Diagnosis prediction
• - Finance: Fraud detection
• - Retail: Forecasting
• - Manufacturing: Maintenance
• - Business: Automated analytics

8. My Experience with AutoML on
Kaggle
• Used [AutoML tool] for a
[classification/regression] problem
• Process: Preprocessing → AutoML Pipeline →
Evaluation
• Outcome: Competitive model, great learning
experience

9. Pros & Cons of AutoML
• **Advantages:**
• - Time-saving
• - No ML expertise required
• - High-performing models
• **Limitations:**
• - Black-box behavior
• - Less control
• - Resource-heavy

10. Conclusion & Q&A
• AutoML democratizes AI by reducing
complexity and accelerating development. It's
transforming industries by making machine
learning more accessible.
• Thank you! Any questions?