Book Image

Machine Learning Algorithms - Second Edition

Book Image

Machine Learning Algorithms - Second Edition

Overview of this book

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
A Gentle Introduction to Machine Learning
A Gentle Introduction to Machine Learning
Introduction – classic and adaptive machines
Only learning matters
Beyond machine learning – deep learning and bio-inspired adaptive systems
Machine learning and big data
Summary
2
Important Elements in Machine Learning
Important Elements in Machine Learning
Data formats
Learnability
Introduction to statistical learning concepts
Class balancing
Elements of information theory
Summary
3
Feature Selection and Feature Engineering
Feature Selection and Feature Engineering
scikit-learn toy datasets
Creating training and test sets
Managing categorical data
Managing missing features
Data scaling and normalization
Feature selection and filtering
Principal Component Analysis
Independent Component Analysis
Atom extraction and dictionary learning
Visualizing high-dimensional datasets using t-SNE
Summary
4
Regression Algorithms
Regression Algorithms
Linear models for regression
A bidimensional example
Linear regression with scikit-learn and higher dimensionality
Ridge, Lasso, and ElasticNet
Robust regression
Bayesian regression
Polynomial regression
Isotonic regression
Summary
5
Linear Classification Algorithms
Linear Classification Algorithms
Linear classification
Logistic regression
Implementation and optimizations
Stochastic gradient descent algorithms
Passive-aggressive algorithms
Finding the optimal hyperparameters through a grid search
Classification metrics
ROC curve
Summary
6
Naive Bayes and Discriminant Analysis
Naive Bayes and Discriminant Analysis
Bayes' theorem
Naive Bayes classifiers
Naive Bayes in scikit-learn
Discriminant analysis
Summary
7
Support Vector Machines
Support Vector Machines
Linear SVM
SVMs with scikit-learn
Kernel-based classification
ν-Support Vector Machines
Support Vector Regression
Introducing semi-supervised Support Vector Machines (S3VM)
Summary
8
Decision Trees and Ensemble Learning
Decision Trees and Ensemble Learning
Binary Decision Trees
Decision Tree classification with scikit-learn
Decision Tree regression
Introduction to Ensemble Learning
Summary
9
Clustering Fundamentals
Clustering Fundamentals
Clustering basics
k-NN
Gaussian mixture
K-means
Evaluation methods based on the ground truth
Summary
10
Advanced Clustering
Advanced Clustering
DBSCAN
Spectral Clustering
Online Clustering
Biclustering
Summary
11
Hierarchical Clustering
Hierarchical Clustering
Hierarchical strategies
Agglomerative Clustering
Summary
12
Introducing Recommendation Systems
Introducing Recommendation Systems
Naive user-based systems
Content-based systems
Model-free (or memory-based) collaborative filtering
Model-based collaborative filtering
Summary
13
Introducing Natural Language Processing
Introducing Natural Language Processing
NLTK and built-in corpora
The Bag-of-Words strategy
Part-of-Speech
A sample text classifier based on the Reuters corpus
Summary
14
Topic Modeling and Sentiment Analysis in NLP
Topic Modeling and Sentiment Analysis in NLP
Topic modeling
Introducing Word2vec with Gensim
Sentiment analysis
Summary
15
Introducing Neural Networks
Introducing Neural Networks
Deep learning at a glance
MLPs with Keras
Summary
16
Advanced Deep Learning Models
Advanced Deep Learning Models
Deep model layers
An example of a deep convolutional network with Keras
An example of an LSTM network with Keras
A brief introduction to TensorFlow
Summary
17
Creating a Machine Learning Architecture
Creating a Machine Learning Architecture
Machine learning architectures
Scikit-learn tools for machine learning architectures
Summary
18
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Ridge, Lasso, and ElasticNet

In this section, we are going to analyze the most common regularization methods and how they can impact the performance of a linear regressor. In real-life scenarios, it's very common to work with dirty datasets, containing outliers, inter-dependent features, and different sensitivity to noise. These methods can help the data scientist mitigate the problems, yielding more effective and accurate solutions.

Ridge

Ridge regression (also known as Tikhonov regularization) imposes an additional shrinkage penalty to the ordinary least squares cost function to limit its squared L2 norm:

X is a matrix containing all samples as rows and the term θ represents the weight vector. The additional...

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