Machine Learning Algorithms

Build strong foundation for entering the world of machine learning and data science with the help of this comprehensive guide

Machine Learning Algorithms

This ebook is included in a Mapt subscription
Giuseppe Bonaccorso

Build strong foundation for entering the world of machine learning and data science with the help of this comprehensive guide
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Book Details

ISBN 139781785889622
Paperback360 pages

Book Description

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.

In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously.

On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem.

Table of Contents

Chapter 1: 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
Further reading
Summary
Chapter 2: Important Elements in Machine Learning
Data formats
Learnability
Statistical learning approaches
Elements of information theory
References
Summary
Chapter 3: 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
Atom extraction and dictionary learning
References
Summary
Chapter 4: Linear Regression
Linear models
A bidimensional example
Linear regression with scikit-learn and higher dimensionality
Ridge, Lasso, and ElasticNet
Robust regression with random sample consensus
Polynomial regression
Isotonic regression
References
Summary
Chapter 5: Logistic Regression
Linear classification
Logistic regression
Implementation and optimizations
Stochastic gradient descent algorithms
Finding the optimal hyperparameters through grid search
Classification metrics
ROC curve
Summary
Chapter 6: Naive Bayes
Bayes' theorem
Naive Bayes classifiers
Naive Bayes in scikit-learn
References
Summary
Chapter 7: Support Vector Machines
Linear support vector machines
scikit-learn implementation
Controlled support vector machines
Support vector regression
References
Summary
Chapter 8: Decision Trees and Ensemble Learning
Binary decision trees
Decision tree classification with scikit-learn
Ensemble learning
References
Summary
Chapter 9: Clustering Fundamentals
Clustering basics
Evaluation methods based on the ground truth
References
Summary
Chapter 10: Hierarchical Clustering
Hierarchical strategies
Agglomerative clustering
References
Summary
Chapter 11: Introduction to Recommendation Systems
Naive user-based systems
Content-based systems
Model-free (or memory-based) collaborative filtering
Model-based collaborative filtering
References
Summary
Chapter 12: Introduction to Natural Language Processing
NLTK and built-in corpora
The bag-of-words strategy
A sample text classifier based on the Reuters corpus
References
Summary
Chapter 13: Topic Modeling and Sentiment Analysis in NLP
Topic modeling
Sentiment analysis
References
Summary
Chapter 14: A Brief Introduction to Deep Learning and TensorFlow
Deep learning at a glance
A brief introduction to TensorFlow
A quick glimpse inside Keras
References
Summary
Chapter 15: Creating a Machine Learning Architecture
Machine learning architectures
scikit-learn tools for machine learning architectures
References
Summary

What You Will Learn

  • Acquaint yourself with important elements of Machine Learning
  • Understand the feature selection and feature engineering process
  • Assess performance and error trade-offs for Linear Regression
  • Build a data model and understand how it works by using different types of algorithm
  • Learn to tune the parameters of Support Vector machines
  • Implement clusters to a dataset
  • Explore the concept of Natural Processing Language and Recommendation Systems
  • Create a ML architecture from scratch.

Authors

Table of Contents

Chapter 1: 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
Further reading
Summary
Chapter 2: Important Elements in Machine Learning
Data formats
Learnability
Statistical learning approaches
Elements of information theory
References
Summary
Chapter 3: 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
Atom extraction and dictionary learning
References
Summary
Chapter 4: Linear Regression
Linear models
A bidimensional example
Linear regression with scikit-learn and higher dimensionality
Ridge, Lasso, and ElasticNet
Robust regression with random sample consensus
Polynomial regression
Isotonic regression
References
Summary
Chapter 5: Logistic Regression
Linear classification
Logistic regression
Implementation and optimizations
Stochastic gradient descent algorithms
Finding the optimal hyperparameters through grid search
Classification metrics
ROC curve
Summary
Chapter 6: Naive Bayes
Bayes' theorem
Naive Bayes classifiers
Naive Bayes in scikit-learn
References
Summary
Chapter 7: Support Vector Machines
Linear support vector machines
scikit-learn implementation
Controlled support vector machines
Support vector regression
References
Summary
Chapter 8: Decision Trees and Ensemble Learning
Binary decision trees
Decision tree classification with scikit-learn
Ensemble learning
References
Summary
Chapter 9: Clustering Fundamentals
Clustering basics
Evaluation methods based on the ground truth
References
Summary
Chapter 10: Hierarchical Clustering
Hierarchical strategies
Agglomerative clustering
References
Summary
Chapter 11: Introduction to Recommendation Systems
Naive user-based systems
Content-based systems
Model-free (or memory-based) collaborative filtering
Model-based collaborative filtering
References
Summary
Chapter 12: Introduction to Natural Language Processing
NLTK and built-in corpora
The bag-of-words strategy
A sample text classifier based on the Reuters corpus
References
Summary
Chapter 13: Topic Modeling and Sentiment Analysis in NLP
Topic modeling
Sentiment analysis
References
Summary
Chapter 14: A Brief Introduction to Deep Learning and TensorFlow
Deep learning at a glance
A brief introduction to TensorFlow
A quick glimpse inside Keras
References
Summary
Chapter 15: Creating a Machine Learning Architecture
Machine learning architectures
scikit-learn tools for machine learning architectures
References
Summary

Book Details

ISBN 139781785889622
Paperback360 pages
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