Demystifying Machine Learning
summary
A plain walkthrough that builds a salary predictor from a CSV of ~50 employees using Jupyter Lab, pandas and scikit-learn linear regression. The pipeline: split with train_test_split, fit coefficients (theta0, theta1), plot the regression line with matplotlib, predict on X_test and compare to y_test — examples show prediction errors like 3,741 and 122. Covers basic linear algebra, supervised vs unsupervised labels, where ML fits, and notes the model is deployable to cloud.
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chapters (4)
- 0:00Introduction and concepts
- 14:50Machine learning workflow
- 16:33Coding demonstration
- 25:44Conclusion and contact
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