A developers guide to Machine Learning
summary
Begins as a practical beginner’s guide: gathers real Swedish house listings, makes scatterplots, fits linear regression and then decision-forest ensembles, and reports an example score of R^2 = 0.77. Walks through getting data from Kaggle/UCI, using Python notebooks and Azure ML, shows feature engineering (GPS → distance to water), warns that 80–90% of the work is data cleaning, demonstrates train/test and cross‑validation to avoid overfitting, and gives concrete failure modes (models learning snow in images or defaulted performance scores).
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chapters (26)
- 0:00Start
- 0:29Machine Learning
- 6:44Scatterplot
- 7:24Linear Regression
- 8:22The Confidence Interval
- 11:44Classification
- 12:21Anomaly Detection
- 12:57Supervised Learning
- 13:20Clustering
- 15:08Reinforcement Learning
- 22:27Python Notebook
- 25:00Outliers
- 26:24Clean the Data
- 35:08Algorithms in Machine Learning
- 35:20Decision Tree
- 35:42Decision Trees
- 37:57Shallow Decision Trees
- 40:01Logistic Regression
- 40:43Neural Networks
- 46:10Introduction to Neural Networks
- 46:23What Algorithm Should You Use
- 47:04Hyper Parameters
- 47:57Train the Model
- 48:48Overfitting
- 49:34Train the Models
- 53:58Coefficient of Determination
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