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Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning (English Edition) 1° Edizione, Formato Kindle
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide
- Get 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.
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.
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.
Table of Contents
- A Gentle Introduction to Machine Learning
- Important Elements in Machine Learning
- Feature Selection and Feature Engineering
- Linear Regression
- Logistic Regression
- Naive Bayes
- Support Vector Machines
- Decision Trees and Ensemble Learning
- Clustering Fundamentals
- Hierarchical Clustering
- Introduction to Recommendation Systems
- Introduction to Natural Language Processing
- Topic Modeling and Sentiment Analysis in NLP
- A Brief Introduction to Deep Learning and TensorFlow
- Creating a Machine Learning Architecture
Esiste una versione più recente di questo articolo:
Giuseppe Bonaccorso is a machine learning and big data consultant with more than 12 years of experience. He has an M.Eng. in electronics engineering from the University of Catania, Italy, and further postgraduate specialization from the University of Rome, Tor Vergata, Italy, and the University of Essex, UK. During his career, he has covered different IT roles in several business contexts, including public administration, military, utilities, healthcare, diagnostics, and advertising. He has developed and managed projects using many technologies, including Java, Python, Hadoop, Spark, Theano, and TensorFlow. His main interests on artificial intelligence, machine learning, data science, and philosophy of mind.--Questo testo si riferisce a un'edizione fuori stampa o non disponibile di questo titolo.
- ASIN : B072QBG11J
- Editore : Packt Publishing; 1° edizione (24 luglio 2017)
- Lingua : Inglese
- Dimensioni file : 40819 KB
- Da testo a voce : Abilitato
- Screen Reader : Supportato
- Miglioramenti tipografici : Abilitato
- X-Ray : Non abilitato
- Word Wise : Non abilitato
- Lunghezza stampa : 362 pagine
- Numeri di pagina fonte ISBN : 1785889621
- Recensioni dei clienti:
Informazioni sugli autori
Le recensioni migliori da altri paesi
1. Book does justice to introduce you to the basics of Machine Learning algorithms.
2. Mathematics is not kept at the center of the book, most of the concepts are explained into more of the theoretical sense than mathematically (This might be a disadvantage to the people looking at this book from a mathematical perspective).
3. The good part of the book is, it explains the application of algorithms and techniques with python code examples.
(sklearn is the library of choice mostly).
1. Less focus on mathematical derivations of the algorithms.
2. Less information about deep learning.
But since this is just an introductory book, Cons are justifiable.