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Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide
Key FeaturesGet started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.Book DescriptionAs 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.
What you will learnAcquaint yourself with important elements of Machine LearningUnderstand the feature selection and feature engineering processAssess performance and error trade-offs for Linear RegressionBuild a data model and understand how it works by using different types of algorithmLearn to tune the parameters of Support Vector machinesImplement clusters to a datasetExplore the concept of Natural Processing Language and Recommendation SystemsCreate a ML architecture from scratch.Table of ContentsA Gentle Introduction to Machine LearningImportant Elements in Machine LearningFeature Selection and Feature EngineeringLinear RegressionLogistic RegressionNaive BayesSupport Vector MachinesDecision Trees and Ensemble LearningClustering FundamentalsHierarchical ClusteringIntroduction to Recommendation SystemsIntroduction to Natural Language ProcessingTopic Modeling and Sentiment Analysis in NLPA Brief Introduction to Deep Learning and TensorFlowCreating a Machine Learning Architecture
Key FeaturesGet started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.Book DescriptionAs 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.
What you will learnAcquaint yourself with important elements of Machine LearningUnderstand the feature selection and feature engineering processAssess performance and error trade-offs for Linear RegressionBuild a data model and understand how it works by using different types of algorithmLearn to tune the parameters of Support Vector machinesImplement clusters to a datasetExplore the concept of Natural Processing Language and Recommendation SystemsCreate a ML architecture from scratch.Table of ContentsA Gentle Introduction to Machine LearningImportant Elements in Machine LearningFeature Selection and Feature EngineeringLinear RegressionLogistic RegressionNaive BayesSupport Vector MachinesDecision Trees and Ensemble LearningClustering FundamentalsHierarchical ClusteringIntroduction to Recommendation SystemsIntroduction to Natural Language ProcessingTopic Modeling and Sentiment Analysis in NLPA Brief Introduction to Deep Learning and TensorFlowCreating a Machine Learning Architecture
362 pages, Kindle Edition
First published July 1, 2017
13 people are currently reading
54 people want to read
About the author
Giuseppe Bonaccorso
18 books3 followersExperienced and goal-oriented senior executive leader with wide expertise in the management of Artificial Intelligence, Machine Learning, Deep Learning, and Data Science projects for healthcare, B2C and Military industries (Fortune 500 firms).
His main interests include Machine/Deep Learning, Reinforcement Learning, Advanced Analytics, Bio-inspired adaptive systems, Business Intelligence, Neuroscience, Neural Language Processing, Econometrics, Data Science Strategy and Organization.
Professional member of IEEE, IEEE Computer Society, AAAI, ACM, IAENG, AICA, SFIA, and Agile Manifesto.
His main interests include Machine/Deep Learning, Reinforcement Learning, Advanced Analytics, Bio-inspired adaptive systems, Business Intelligence, Neuroscience, Neural Language Processing, Econometrics, Data Science Strategy and Organization.
Professional member of IEEE, IEEE Computer Society, AAAI, ACM, IAENG, AICA, SFIA, and Agile Manifesto.
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