Book review: Python Machine Learning
 Book: Python Machine Learning
 Author: Sebastian Raschka
 Github
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 WidrowHoff 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 ScikitLearn
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 (logodds), \[logit(p) = \log \frac{p}{1  p}\]
 Learning the weights of the logistic cost function
 Tackling overfitting via regularization
 Large scale machine learning and stochastic gradient descent
Notebooks:
Code: