Machine Learning Algorithms
暫譯: 機器學習演算法

Key Features

• 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 machine learning algorithms, like the types, working, and implementation.
• Get solid foundation to your entry in machine learning by strengthening your roots i.e the algorithms with this comprehensive guide.

Book Description

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. Few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, XGBooster and Feature engineering. In this book you will also learn the working and the practical implementation of these algorithms to resolve your problems. This book will also introduce you to Natural Processing Language and Recommendation systems, which help you to run multiple algorithms simultaneously.

On completion of the book you will understand how to pick machine learning algorithm for clustering, classification, or regression best suited 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 of Linear Regression
• Build a data model and understand how it works by using different types of algorithms
• Learn to tune parameters of Support Vector machines
• Implement clusters to a data set
• Explore the concept of Natural Processing Language and Recommendation Systems
• Create a ML architecture from scratch.