Start With The Question
Before names or math, ask: What do you want to discover? Pick the right tool, plus a bit of care, and you get useful answers.
Predicting Numbers?
Supervised > RegressionPredicting Categories?
Supervised > ClassificationFinding Groups?
Unsupervised > ClusteringComplex Patterns?
Deep Learning“The first time I trained a model, it felt like teaching a curious puppy to fetch—clumsy at first, then surprisingly good.”
Supervised Learning
Learning from examples that have the “right” answer. Like a student with an answer key.
Linear Regression
The “Ruler”
Think of a ruler through dots. Tries to draw the best straight line. Great for quick baselines like housing prices.
Logistic Regression
The “Yes/No”
Estimates the chance something is A or B. Will a customer churn? Is this spam? Clean and reliable.
Trees & Forests
The “Flowchart”
Splits data into simple rules (If > X then Y). Random Forests use many trees to vote, increasing stability.
Unsupervised Learning
Finding structure without answers. Looking for shapes in the fog.
Clustering (k-Means)
segmentation
Imagine tossing magnets on a metal sheet. Points pull toward the nearest magnet. Great for grouping similar customers.
PCA (Dimensionality)
simplification
Like folding a complex map. It reshapes the data space so most variation fits into fewer directions.
Deep Learning
When data is rich and messy (Images, Sound, Text). Neural networks shine here.
CNN
Convolutional Neural Networks scan images with filters. They catch edges, textures, shapes.
Transformers
The modern standard for text. They pay attention to all words at once. Powered by massive compute.
Neural Nets
Stacked layers of simple units. Can surpass classic models when signal is complex.
How To Judge Models
Overfitting is the classic pothole. You crush the training set, then stumble on new data. It’s like memorizing the answers instead of learning the subject.
Precision vs Recall
Precision: No false alarms.
Recall: Don’t miss the bad guys.
(Choose based on what hurts more.)
Split Fairly
The Sweet Spot (Bias vs Variance)
A Quick Field Guide
What to try first when you land on a new planet.
| Scenario | Start With… | Then Try… |
|---|---|---|
| Tabular Data (Excel/SQL) | Linear / Logistic Regression | Random Forest or Gradient Boosting |
| Few rows, many columns | Regression + Regularization | PCA (Simplify) before modeling |
| Images | Small CNN | Pre-trained ResNet/EfficientNet |
| Text / NLP | Bag-of-Words | Transformers (BERT/GPT) |
| Anomalies | Isolation Forest | Simple Thresholds |
Keep It Responsible
Models touch people. Check for bias. Monitor drift. Explain choices. A model that is fair and stable earns trust.
“Features beat fancy. Clean data wins.”
