Book review: Python Machine Learning

1. Machine Learning - Giving Computers the Ability to Learn from Data

Overview:

Intro to three different types of machine learning:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Notebooks:

2. Training Machine Learning Algorithms for Classification

Overview:

  • Perceptron
    • the activation function is a simple unit step function, which is sometimes also called the Heaviside step function:
  • Adaptive Linear Neuron
    • The key difference between the Adaline rule (also known as the Widrow-Hoff rule) and Rosenblatt’s perceptron is that the weights are updated based on a linear activation function rather than a unit step function like in the perceptron. In Adaline, this linear activation function is simply the identity function of the net input so that .
    • While the linear activation function is used for learning the weights, a quantizer, which is similar to the unit step function that we have seen before, can then be used to predict the class labels.

Notebooks:

Code:

3. A Tour of Machine Learning Classifiers Using Scikit-Learn

Overview:

  • Logistic regression intuition and conditional probabilities
    • odds ratio: which is the odds in favor of a particular event,

\[\frac{p}{1 - p}\], where p stands for the probability of the positive (1− p) event.

  • logit function: the logarithm of the odds ratio (log-odds), \[logit(p) = \log \frac{p}{1 - p}\]

  • Learning the weights of the logistic cost function
  • Tackling overfitting via regularization
  • Large scale machine learning and stochastic gradient descent

Notebooks:

Code: