Название: Machine Learning For Beginners: A Math Free Introduction for Business and Individuals to Machine Learning, Big Data, Data Science, and Neural Networks Автор: Konnor Cluster
Издательство: Amazon Digital Services LLC
Год: 2019
Страниц: 103
Язык: английский
Формат: epub, azw3, pdf (conv)
Размер: 10.1 MB
Если вам нужно узнать, как использовать машинное обучение, большие данные, науку о данных и нейронные сети, но вы не можете (или нет времени) изучать сложные математические алгоритмы, которые стоят за этими технологиями, то прочитайте эту книгу.
If you need to learn how to use Machine Learning, Big Data, Data Science and Neural Networks but you can’t (or there is no time to) study the complicated math and algorithms behind these technologies, then keep reading.
The beginning of this guide is going to take some time to look at what Data Science is all about and how this is going to relate to Machine Learning (ML). Then we will move on to some of the information that we need to know when it comes to Machine Learning, along with the three different types of Machine Learning including supervised machine learning, unsupervised machine learning, and reinforcement machine learning.
Once we have a better understanding of what Machine Learning is all about, we are going to take a look at some of the common techniques that we are able to use when it comes to Machine Learning. This book is full of some of the best machine learning algorithms that are available, some of the times when we would want to use each of them, and how they work. There are a ton of machine learning algorithms that we are able to work with, and your choice is going to depend on a few factors, including what data you want to sort through, how much data, and what results you are looking to get. We will discuss all this and more in this guide.
The end of this guide is going to take a look at some of the workflow of Data Science and how to get started with one of your data science projects when using Machine Learning, along with some discussion on what you are able to do with Big Data.
Contents:
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