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Introduction
Foundations of Machine Learning
Overview and Importance of No-Code Machine Learning
Scope of Machine Learning
Core Components of Machine Learning
Introduction to Deep Learning
Comparison of ML and DL
Applications of AI, ML, and DL
Types of Machine Learning
Rule-based vs Data-driven system
Model fit
Problem Definition and Data Collection
Data Preprocessing
Titanic Dataset
Demo: Kaggle
Demo: Data Import and Feature Selection
Demo: Handling Missing Values
Demo: Data Normalization
Demo: Data Standardization
Demo: Data Flow
Demo: Model Building
Demo: Modeling Algorithms
Demo: Model Training
Demo: Model Explainability
Demo: Confusion Matrix
Demo: Decision Charts
Demo: Lift Charts
Demo: ROC & PR Curves
Demo: Model Information
Feature engineering
Model Selection
Testing and Validation
Deployment
Demo: Model Deployment
Demo: Model Score
Demo: Score Test
Monitoring and Maintenance
Using API Service in Dataiku for model prediction
Demo: Dataiku API Services
Demo: Model Deployment and Compare Evaluation
Demo: Export to Jupyter Notebook
No-Code Machine Learning Tools and Applications
Introduction to No-Code Machine Learning
Motivation for No-Code ML
Demo: No Code Platform Introduction
Working with No-Code Machine Learning Tools
Working with Data in No-Code ML Platforms
Demo: Data Import into No Code Platform
Demo: IP Address to Address
IP Address
Demo: Geopoints and Distance
housing
Demo: Sentiment Analysis - Preprocess Text
sentiment-analysis
Demo: Sentiment Analysis - Model Build
Building Models with No-Code Tools
Advanced Topics in No-Code Machine Learning
Machine Learning Challenges
Model Interpretability
Computational Cost and ethical consideration
Demo: Model Fairness Report