This is the second in a series of posts in which I explore concepts in Andrew Ng’s Introduction to Machine Learning course on Coursera. In each, I’m implementing a machine learning algorithm in Python: first using standard Python data science and numerical libraries, and then with TensorFlow.
The algorithm described here is logistic regression. Logistic regression is recognizably similar to linear regression, but instead of predicting a continuous output, classifies training examples by a set of categories or labels.
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