Machine learning algorithms reference guide for popular algorithms for data science and machine learning
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 str...
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
Birmingham
PACKT Publishing
2017
Packt Pub Packt Publishing, Limited Packt Publishing Limited |
| Vydání: | 1st ed. |
| Témata: | |
| ISBN: | 9781785889622, 1785889621, 1785884514, 9781785884511 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Bonaccorso, Giuseppe |
| Author_xml | – sequence: 1 fullname: Bonaccorso, Giuseppe |
| BackLink | https://cir.nii.ac.jp/crid/1130000794861505792$$DView record in CiNii |
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| DOI | 10.0000/9781785884511 |
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| Discipline | Computer Science |
| EISBN | 1785884514 9781785884511 |
| Edition | 1st ed. 1 |
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| Notes | Includes bibliographical references and index |
| OCLC | 999648897 |
| PQID | EBC4926962 |
| PageCount | 352 |
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| PublicationDate | 2017. 2017 [2017] |
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| PublicationDecade | 2010 |
| PublicationPlace | Birmingham |
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| PublicationYear | 2017 |
| Publisher | PACKT Publishing Packt Pub Packt Publishing, Limited Packt Publishing Limited |
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| Snippet | 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... |
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| SubjectTerms | Big Data and Business Intelligence COM018000 COMPUTERS / Data Processing Computer algorithms COMPUTERS / Programming / Algorithms COMPUTERS / Programming Languages / Python Machine learning |
| SubjectTermsDisplay | Big Data and Business Intelligence Computer algorithms Machine learning |
| Subtitle | reference guide for popular algorithms for data science and machine learning |
| TableOfContents | Machine learning algorithms: reference guide for popular algorithms for data science and machine learning -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- 1. A Gentle Introduction to Machine Learning -- 2. Important Elements in Machine Learning -- 3. Feature Selection and Feature Engineering -- 4. Linear Regression -- 5. Logistic Regression -- 6. Naive Bayes -- 7. Support Vector Machines -- 8. Decision Trees and Ensemble Learning -- 9. Clustering Fundamentals -- 10. Hierarchical Clustering -- 11. Introduction to Recommendation Systems -- 12. Introduction to Natural Language Processing -- 13. Topic Modeling and Sentiment Analysis in NLP -- 14. A Brief Introduction to Deep Learning and TensorFlow -- 15. Creating a Machine Learning Architecture -- Index. Alternating least squares with Apache Spark MLlib -- References -- Summary -- Chapter 12: Introduction to Natural Language Processing -- NLTK and built-in corpora -- Corpora examples -- The bag-of-words strategy -- Tokenizing -- Sentence tokenizing -- Word tokenizing -- Stopword removal -- Language detection -- Stemming -- Vectorizing -- Count vectorizing -- N-grams -- Tf-idf vectorizing -- A sample text classifier based on the Reuters corpus -- References -- Summary -- Chapter 13: Topic Modeling and Sentiment Analysis in NLP -- Topic modeling -- Latent semantic analysis -- Probabilistic latent semantic analysis -- Latent Dirichlet Allocation -- Sentiment analysis -- VADER sentiment analysis with NLTK -- References -- Summary -- Chapter 14: A Brief Introduction to Deep Learning and TensorFlow -- Deep learning at a glance -- Artificial neural networks -- Deep architectures -- Fully connected layers -- Convolutional layers -- Dropout layers -- Recurrent neural networks -- A brief introduction to TensorFlow -- Computing gradients -- Logistic regression -- Classification with a multi-layer perceptron -- Image convolution -- A quick glimpse inside Keras -- References -- Summary -- Chapter 15: Creating a Machine Learning Architecture -- Machine learning architectures -- Data collection -- Normalization -- Dimensionality reduction -- Data augmentation -- Data conversion -- Modeling/Grid search/Cross-validation -- Visualization -- scikit-learn tools for machine learning architectures -- Pipelines -- Feature unions -- References -- Summary -- Index Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: A Gentle Introduction to Machine Learning -- Introduction - classic and adaptive machines -- Only learning matters -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- 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 -- Multiclass strategies -- One-vs-all -- One-vs-one -- Learnability -- Underfitting and overfitting -- Error measures -- PAC learning -- Statistical learning approaches -- MAP learning -- Maximum-likelihood learning -- 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 -- Non-negative matrix factorization -- Sparse PCA -- Kernel PCA -- Atom extraction and dictionary learning -- References -- Summary -- Chapter 4: Linear Regression -- Linear models -- A bidimensional example -- Linear regression with scikit-learn and higher dimensionality -- Regressor analytic expression -- 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 -- Bernoulli naive Bayes -- Multinomial naive Bayes -- Gaussian naive Bayes -- References -- Summary -- Chapter 7: Support Vector Machines -- Linear support vector machines -- scikit-learn implementation -- Linear classification -- Kernel-based classification -- Radial Basis Function -- Polynomial kernel -- Sigmoid kernel -- Custom kernels -- Non-linear examples -- Controlled support vector machines -- Support vector regression -- References -- Summary -- Chapter 8: Decision Trees and Ensemble Learning -- Binary decision trees -- Binary decisions -- Impurity measures -- Gini impurity index -- Cross-entropy impurity index -- Misclassification impurity index -- Feature importance -- Decision tree classification with scikit-learn -- Ensemble learning -- Random forests -- Feature importance in random forests -- AdaBoost -- Gradient tree boosting -- Voting classifier -- References -- Summary -- Chapter 9: Clustering Fundamentals -- Clustering basics -- K-means -- Finding the optimal number of clusters -- Optimizing the inertia -- Silhouette score -- Calinski-Harabasz index -- Cluster instability -- DBSCAN -- Spectral clustering -- Evaluation methods based on the ground truth -- Homogeneity -- Completeness -- Adjusted rand index -- References -- Summary -- Chapter 10: Hierarchical Clustering -- Hierarchical strategies -- Agglomerative clustering -- Dendrograms -- Agglomerative clustering in scikit-learn -- Connectivity constraints -- References -- Summary -- Chapter 11: Introduction to Recommendation Systems -- Naive user-based systems -- User-based system implementation with scikit-learn -- Content-based systems -- Model-free (or memory-based) collaborative filtering -- Model-based collaborative filtering -- Singular Value Decomposition strategy -- Alternating least squares strategy Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning |
| Title | Machine learning algorithms |
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