Learning Objectives

• Define a machine learning problem
• Collect and understand the dataset
• Set up project structure
• Define business problem
• Identify data sources
• Set up Python environment
• Create GitHub repository

pseudo
ml-project/
├── data/
│   ├── raw/          # Original data
│   ├── processed/    # Cleaned data
│   └── external/     # External data
├── notebooks/        # Jupyter notebooks
├── src/
│   ├── data/         # Data processing
│   ├── features/     # Feature engineering
│   ├── models/       # Model training
│   └── visualization/ # Plots
├── models/           # Saved models
├── reports/          # Output reports
└── README.md

1. Clear objective: Predict/classify/recommend what?
2. Measurable success metrics: Accuracy, RMSE, F1?
3. Available data: Do you have enough labeled data?
4. Baseline: What's the simplest approach?

Q1: What makes a good ML project?

Well-defined problem, available data, measurable metrics, baseline exists, feasible with available resources, has real-world application. Q2: How to choose between classification and regression?

Classification: predict discrete category (spam/not spam). Regression: predict continuous value (house price). Q3: What is a baseline model?

Simple model to beat (majority class, mean value, linear regression). If your complex model doesn't beat baseline, something is wrong. Q4: How much data do you need?

Depends on problem complexity, model complexity, feature dimensionality. Rule of thumb: 10x more samples than features. Deep learning needs 1000x more. Join Discord NextMilestone 2: Exploratory Data Analysis (EDA)