| Edition |
Second edition. |
| Description |
130 pages : illustrations ; 23 cm |
| Contents |
Introduction -- From Data Science, to AI, to Machine Learning -- Self-Learning -- Tools -- Machine Learning Categories -- Machine Learning in Action -- Regression Analysis -- Clustering Analysis -- Dimensionality Reduction -- Support Vector Machines -- Artificial Neural Networks -- Bias & Variance -- Decision Trees -- Association Analysis -- Recommender Systems -- Algorithm Selection -- Development Environment -- Building a Model in Python -- Career & Study Options -- Further Resources -- Downloading Datasets. |
| Summary |
"The manner in which computers are now able to mimic human thinking to process information is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the modern age of machine learning, computers do not strictly need to receive an 'input command' to perform a task, but rather 'input data'. From the input of data they are able to form their own decisions and take actions virtually as a human world. But given it is a machine, it can consider many more scenarios and execute far more complicated calculations to solve complex problems. This is the element that excites data scientists and machine learning engineers the most. The ability to solve complex problems never before attempted. This book will dive in to introduce machine learning, and is ideal for beginners starting out in machine learning."--page 4 of cover. |
| Subject |
Machine learning.
|
|
Machine learning. |
|
Computer algorithms.
|
|
Computer algorithms. |
|
Data mining.
|
|
Data mining. |
| ISBN |
152095140X (paperback) |
|
9781520951409 (paperback) |
|
