What Is Machine Learning
Machine learning is teaching computers to find patterns in data instead of writing rules by hand.
Linear Regression from Scratch
Linear regression draws the best straight line through your data. It is the "hello world" of machine learning.
Logistic Regression & Classification
Logistic regression bends a straight line into an S-curve to answer yes-or-no questions with probabilities.
Decision Trees & Random Forests
A decision tree is just a flowchart. But a forest of them is one of the most powerful tools in ML.
Support Vector Machines
Find the widest street between two classes. That is the entire idea.
KNN & Distance Metrics
Store everything. Predict by looking at your neighbors. The simplest algorithm that actually works.
Unsupervised Learning: K-Means, DBSCAN
No labels, no teacher. The algorithm finds structure on its own.
Feature Engineering & Selection
A good feature is worth a thousand data points.
Model Evaluation: Metrics, Cross-Validation
A model is only as good as the way you measure it.
Bias, Variance & the Learning Curve
Every model error comes from one of three sources: bias, variance, or noise. You can only control the first two.
Ensemble Methods: Boosting, Bagging, Stacking
A group of weak learners, combined correctly, becomes a strong learner. This is not a metaphor. It is a theorem.
Hyperparameter Tuning
Hyperparameters are the knobs you turn before training starts. Turning them well is the difference between a mediocre model and a great one.
ML Pipelines & Experiment Tracking
A model is not a product. A pipeline is. The pipeline is everything from raw data to deployed prediction, and every step must be reproducible.
Naive Bayes
The "naive" assumption is wrong, and it works anyway. That's the beauty of it.
Time Series Fundamentals
Past performance does predict future results -- if you check for stationarity first.
Anomaly Detection
Normal is easy to define. Abnormal is whatever doesn't fit.
Handling Imbalanced Data
When 99% of your data is "normal," accuracy is a lie.
Feature Selection
More features is not better. The right features is better.