Machine Learning Algorithms
Packt Publishing Ltd, 24 lug 2017 - 360 pagine
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guideAbout This Book
This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.What You Will Learn
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.Style and approach
An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.
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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
Decision Trees and Ensemble Learning