Advanced Machine Learning

Table of contents

• Table of contents

Part A. General

• 1. Introduction
• 2. Data Pre-Processing
• 3. Transformation Pipeline
• 4. EDA
• 5. Feature Engineering
• 6. Evaluation Metrics
• 7. Model Fine-Tuning
• 8. Model Deployment

Part B. Machine Learning Models

• 1. Ensemble methods
• 2. Stochastic Gradient Descent
• 3. Naïve Bayes
• 4. Linear Regression

Resources

• Resource: Hands-On Machine Learning with Scikit-Learn and TensorFlow

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