Intro to machine learning in Node
summary
Shows working Node.js servers that actually do ML: an Express image classifier using tensorflow/tfjs-node and a MobileNet model, a decision tree on the Melbourne housing CSV using decision-tree, and a Brain.js neural net that classifies Trump‑style tweets. Measures show a synchronous CPU task blocks the event loop (~6 req/s, ~1.5s median) while offloading to the async worker pool improves throughput and latency (~8 req/s, ~1.0s). Recommends worker_threads, queues or separate consumers for CPU‑heavy ML.
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chapters (15)
- 0:00Start
- 8:49What is Machine Learning
- 10:32Some Machine Learning Applications
- 11:40How To Build Machine Learning Model
- 12:39An Example For A Data Set
- 14:10Choosing A Model To Train
- 14:58Lets Predict House Prices With Simple Decision Tree
- 15:30More Sophisticated Decision Tree
- 16:41Lets Fit And Predict The Data
- 25:50Evaluate How Accurate Are The Predictions of Your Model
- 27:00Popular Node.js Machine Learning Libraries
- 28:37TensorFlow.js
- 38:40Basic Unit Of Neural Networks - Artificial Neurons
- 48:36Main Problems
- 49:10Node.js Architecture
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