Название: Machine Learning for Beginners: An Introductory Guide to Learn and Understand Artificial Intelligence, Neural Networks and Machine LearningАвтор: James Deep
Издательство: Amazon Digital Services LLC
Год: 2019
Страниц: 135
Язык: английский
Формат: pdf, rtf
Размер: 10.18 MB
Curious about the world of Machine Learning? Would you like to explore its features? If yes, then keep reading. Machine Learning is defined as the ability of algorithms to work efficiently without explicit programming.
This book dives deep in analyzing the types, models, and applications of Machine Learning algorithms, using a tremendous comparative analysis approach. It sets the tone on pointing out the close relationship between the subsects of Artificial Intelligence and Machine Learning. The book also bears in mind the importance of data in Machine Learning by explaining the terminologies used about and the process of Data Mining. There is no Machine Learning without Data Mining.
But that's not all, because "Machine Learning for Beginners" takes a trip on the current upsurge of intelligent machines and their application in various sectors of the economy. The book covers the field extensively offering recommendations on the suitable road to take for organizations and the general public. It outlines a different model for predictions in a step-by-step slant of the model, of the algorithmic process, of decision making, and solution.
Machine learning is an outgrowth of Statistics and Computer Science. While Computer science focuses primarily on manual programming, machine learning considers the ‘how’ of selfprogramming. On the other hand, statistics focus on conclusions that one can infer from specific data, while machine learning incorporates the idea of computational algorithms and architecture that can help capture, index, store, merge, and retrieve the data. Early researchers established the existence of a close relationship between statistics and machine learning.
Inside this book you will find:
The importance of Artificial intelligence
Types of ML
Neural & Bayesian networks
Machine Learning Models
Support Vector Machine
Components of Soft Computing
Decision Trees classifiers
ML Datasets
Applications of Data Mining
...and many more amazing and interesting topics!
In general terms, this guide is an artifact for people looking to understand the basics of ML, not only from the beginners' viewpoint. If you have not joined the Machine Learning world yet, this is the best moment to do that.
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