Szczegóły ebooka

Machine Learning Algorithms. A reference guide to popular algorithms for data science and machine learning

Machine Learning Algorithms. A reference guide to popular algorithms for data science and machine learning

Giuseppe Bonaccorso

Ebook
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. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.

In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.

On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem
  • 1. Gentle Introduction To Machine Learning
  • 2. Important Elements In A Machine Learning
  • 3. Feature Selection & Feature Engineering
  • 4. Linear Regression
  • 5. Logistic Regression
  • 6. Naïve Baiyes
  • 7. Support Vector Machines
  • 8. Decision Trees And Random Forests
  • 9. K-Means
  • 10. Heirarchical Clustering
  • 11. Introduction To Recommedation Systems
  • 12. Introduction To Natural Language Processing
  • 13. Topic Modelling and Sentiment Analysis in NLP
  • 14. Brief Introduction To Deep Learning And Tensorflow
  • 15. Creating a Machine Learning Architecture
  • Tytuł: Machine Learning Algorithms. A reference guide to popular algorithms for data science and machine learning
  • Autor: Giuseppe Bonaccorso
  • Tytuł oryginału: Machine Learning Algorithms. A reference guide to popular algorithms for data science and machine learning
  • ISBN: 9781785884511, 9781785884511
  • Data wydania: 2017-07-24
  • Format: Ebook
  • Identyfikator pozycji: e_15mn
  • Wydawca: Packt Publishing