Books & Videos

Table of Contents

Chapter: Giving Computers the Ability to Learn from Data

The Course Overview

01m 25s

Transforming Data into Knowledge

04m 43s

Types of Machine Learning

05m 1s

Chapter: Training Machine Learning Algorithms for Classification

Implementing a Perceptron Algorithm in Python

11m 44s

The Iris Dataset

11m 7s

Training the Perceptron

03m 43s

Improving the Visualization

08m 2s

Adaline in Python

15m 16s

Feature Standardization

09m 25s

Implementing Adaline

14m 37s

Chapter: A Tour of Machine Learning Classifiers Using Scikit-Learn

Scikit-Learn Perceptron

15m 45s

Logistic Regression in Scikit-Learn

07m 36s

Predicting Class Probabilities

08m 55s

Training a Support Vector Machine in Scikit-Learn

10m 35s

The Effect of Gamma

06m 32s

Decision Trees

21m 4s

Chapter: Building Good Training Sets – Data Preprocessing

Handling Data

08m 23s

Mapping Ordinal Features

13m 17s

Feature Scaling

15m 49s

Feature Importance's with Random Forests

09m 43s