@machinelearningtogo: 🗂 Remember these 6 algorithms if you want to enter ML ➡️Machine Learning from Scratch, part 23: 6 crucial algorithms 1. Linear regression (part 1): fits the straight line that comes closest to the points. Our 13 lines learned it step by step: error 34.92 -> 0.09 after 1,000 epochs (w 0.80, b 2.00; exact least squares: 0.80 and 2.03). Used to predict numbers, like house prices. 2. Decision tree (part 18): yes or no questions learned from 400 made-up bank customers: income above 3,020? Then: more than one missed payment? 82 of 100 new customers right, and you can read every step. 3. Random forest (part 20): 100 trees, each on its own random sample, then a majority vote. The first new customer got 15 yes and 85 no; 84 of 100 new customers right. A standard tool for table data, like fraud prediction. 4. Gradient boosting (part 22): a first guess of 47%, then 200 small trees, each adding 1% of a fix for what is still wrong: 84 of 100 new customers right. XGBoost is its best known version. 5. K-means (part 17): 600 made-up photos from three trips, no labels. Each photo joins its nearest center, each center moves to the middle of its photos. Try 5 needed 9 rounds and found the trips: city 250, coast 200, mountains 150. 6. Neural network (parts 2 and 3): one neuron draws one line; three neurons learn XOR, which no single line can separate (4 of 4 from epoch 386 on). Bigger networks read images and write text. Our small versions: part 10 reads a test digit as an 8 (90%), part 11 writes "the bird sat on a park." #machinelearning #python #ai #datascience #algorithms

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Saturday 03 October 2026 09:11:47 GMT