InterviewsVector
Original Academy

Legacy mirror · noindex · upstream phase 2

ML Fundamentals

Classical ML — still the backbone of most production AI.

Provenance: this phase outline and its lesson readings are preserved from ai-engineering-from-scratch by Rohit Ghumare under the MIT License. InterviewsVector does not claim authorship. These archive pages remain available for old links and progress, but are excluded from indexing.

Attributed readings

  1. 01What Is Machine LearningMachine learning is teaching computers to find patterns in data instead of writing rules by hand.
  2. 02Linear Regression from ScratchLinear regression draws the best straight line through your data. It is the "hello world" of machine learning.
  3. 03Logistic Regression & ClassificationLogistic regression bends a straight line into an S-curve to answer yes-or-no questions with probabilities.
  4. 04Decision Trees & Random ForestsA decision tree is just a flowchart. But a forest of them is one of the most powerful tools in ML.
  5. 05Support Vector MachinesFind the widest street between two classes. That is the entire idea.
  6. 06KNN & Distance MetricsStore everything. Predict by looking at your neighbors. The simplest algorithm that actually works.
  7. 07Unsupervised Learning: K-Means, DBSCANNo labels, no teacher. The algorithm finds structure on its own.
  8. 08Feature Engineering & SelectionA good feature is worth a thousand data points.
  9. 09Model Evaluation: Metrics, Cross-ValidationA model is only as good as the way you measure it.
  10. 10Bias, Variance & the Learning CurveEvery model error comes from one of three sources: bias, variance, or noise. You can only control the first two.
  11. 11Ensemble Methods: Boosting, Bagging, StackingA group of weak learners, combined correctly, becomes a strong learner. This is not a metaphor. It is a theorem.
  12. 12Hyperparameter TuningHyperparameters are the knobs you turn before training starts. Turning them well is the difference between a mediocre model and a great one.
  13. 13ML Pipelines & Experiment TrackingA model is not a product. A pipeline is. The pipeline is everything from raw data to deployed prediction, and every step must be reproducible.
  14. 14Naive BayesThe "naive" assumption is wrong, and it works anyway. That's the beauty of it.
  15. 15Time Series FundamentalsPast performance does predict future results -- if you check for stationarity first.
  16. 16Anomaly DetectionNormal is easy to define. Abnormal is whatever doesn't fit.
  17. 17Handling Imbalanced DataWhen 99% of your data is "normal," accuracy is a lie.
  18. 18Feature SelectionMore features is not better. The right features is better.