ML1 Week 2: ML Pipelines and Data Analysis

Overview

This week introduces the fundamental components of machine learning pipelines and essential data analysis techniques through visualization.

Learning Objectives

By the end of this session, students will:

• Understand each component of an ML pipeline
• Master the process of feature engineering
• Implement training and inference procedures
• Evaluate model performance effectively
• Create insightful data visualizations

Topics Covered

1. ML Pipeline Components

• Model Architecture
  • Model selection
  • Architecture design
  • Component integration
  • Hyperparameter configuration
• Feature Engineering
  • Data preprocessing
  • Feature selection
  • Feature transformation
  • Feature validation

2. Pipeline Execution

• Training Process
  • Data splitting
  • Model fitting
  • Parameter optimization
  • Validation procedures
• Inference
  • Prediction generation
  • Runtime optimization
  • Batch processing
  • Real-time inference

3. Pipeline Evaluation

• Performance Metrics
  • Accuracy measures
  • Error analysis
  • Model comparison
  • Validation strategies
• Bias Analysis
  • Bias identification
  • Fairness metrics
  • Bias mitigation
  • Model auditing

4. Data Analysis Through Plots

• Distribution Analysis
  • Histograms
  • Density plots
  • Box plots
  • Violin plots
• Relationship Visualization
  • Scatter plots
  • Correlation matrices
  • Pair plots
  • Heat maps
• Time Series Analysis
  • Line plots
  • Trend visualization
  • Seasonal decomposition
  • Autocorrelation plots

Key Takeaways

1. Pipeline structure determines project success
2. Feature engineering is crucial for model performance
3. Proper evaluation ensures reliable results
4. Visualization aids in understanding data patterns

Practical Exercises

1. Build an end-to-end ML pipeline
2. Implement feature engineering techniques
3. Create comprehensive visualizations
4. Evaluate model bias and performance