Machine learning quick reference: quick and essential machine learning hacks for training smart data models

Cover
 Copyright
 Credits
 About the Authors
 About the Reviewers
 www.PacktPub.com
 Customer Feedback
 Table of Contents
 Preface
 Chapter 1: Giving Computers the Ability to Learn from Data
 Building intelligent machines to transform data into knowledge
 The three different types of machine learning
 Making predictions about the future with supervised learning
 Classification for predicting class labels
 Regression for predicting continuous outcomes
 Solving interactive problems with reinforcement learning
 Discovering hidden structures with unsupervised learning. Finding subgroups with clusteringDimensionality reduction for data compression
 Introduction to the basic terminology and notations
 A roadmap for building machine learning systems
 Preprocessing --
getting data into shape
 Training and selecting a predictive model
 Evaluating models and predicting unseen data instances
 Using Python for machine learning
 Installing Python and packages from the Python Package Index
 Using the Anaconda Python distribution and package manager
 Packages for scientific computing, data science, and machine learning
 Summary. Chapter 2: Training Simple Machine Learning Algorithms for ClassificationArtificial neurons --
a brief glimpse into the early history of machine learning
 The formal definition of an artificial neuron
 The perceptron learning rule
 Implementing a perceptron learning algorithm in Python
 An object-oriented perceptron API
 Training a perceptron model on the Iris dataset
 Adaptive linear neurons and the convergence of learning
 Minimizing cost functions with gradient descent
 Implementing Adaline in Python
 Improving gradient descent through feature scaling. Large-scale machine learning and stochastic gradient descentSummary
 Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn
 Choosing a classification algorithm
 First steps with scikit-learn --
training a perceptron
 Modeling class probabilities via logistic regression
 Logistic regression intuition and conditional probabilities
 Learning the weights of the logistic cost function
 Converting an Adaline implementation into an algorithm for logistic regression
 Training a logistic regression model with scikit-learn
 Tackling overfitting via regularization. Maximum margin classification with support vector machinesMaximum margin intuition
 Dealing with a nonlinearly separable case using slack variables
 Alternative implementations in scikit-learn
 Solving nonlinear problems using a kernel SVM
 Kernel methods for linearly inseparable data
 Using the kernel trick to find separating hyperplanes in high-dimensional space
 Decision tree learning
 Maximizing information gain --
getting the most bang for your buck
 Building a decision tree
 Combining multiple decision trees via random forests
 K-nearest neighbors --
a lazy learning algorithm
 Summary. Chapter 4: Building Good Training Sets --
Data Preprocessing.