Learning Machine Learning
summary
Covers hands‑on basics with concrete demos: a Naive Bayes language classifier implemented in ~100 lines of PHP that trains by counting words and classifies English vs French; a sentiment analysis uses a word‑score dictionary (scores −5 to +5) rather than ML; and a genetic algorithm finding four floats that sum to 200 showing populations, mutation, crossover and fitness over hundreds–thousands of generations. Mentions deep learning tools like TensorFlow and cloud offerings (IBM Watson, Google Cloud) and warns PHP reloads data each request so performance limits apply.
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chapters (19)
- 0:00Intro
- 1:55About Joel
- 3:07Agenda
- 4:09Artificial Intelligence
- 5:07PacMan
- 6:53Machine Learning
- 10:27Deep Learning
- 11:33Neural Networks
- 13:00Supervised Learning
- 14:45Unsupervised Learning
- 18:07Classification
- 19:10Tinder
- 20:53Dictionary
- 23:57Sentiment Analysis
- 25:42Genetic Algorithm
- 27:40Recap
- 28:25Demo
- 33:32Example
- 36:08Summary
