 Название: Machine Learning: An Introduction for Beginners, User Guide to Build Intelligent Systems
Автор: Mark Howard
Издательство: Amazon Digital Services LLC
Год: 2018
Страниц: 64
Язык: английский
Формат: epub, rtf, pdf (conv)
Размер: 10.1 MB
Machine Learning is everywhere! So why keep our head buried in the sand when it comes to Machine Learning? It is time for you to start learning what Machine Learning is all about and you can do that with this book!
In this book you are going to learn about:
1. Neural Networks;
2. Python and Machine Learning;
3. Examples of Machine Learning;
4. How Machine Learning is beneficial to you;
and so much more!
Before you can delve into Machine Learning, you have to know what Machine Learning is. Machine Learning is a branch of artificial intelligence that stems from the idea that a system is going to be able to take data, learn from it, identify any patterns that are present, and then make decisions without the intervention of a human. If there is intervention from a human, the intervention is minimal. Using the data that the machine gathers, it is able to analyze the data and then automatically build a model from that data.
So, why is Machine Learning so important? It is important because it has the same factors that helped make Bayesian analysis and data mining popular. With the growing volume of available wide variety of data, the computing process becomes cheaper and ultimately more powerful. This allows businesses to store their data.
Because of all of these factors, it means that models are going to be produced quicker and bigger, and more complex sets of data are going to be analyzed faster with more accurate results delivered. This is going to help companies know where they are going to be able to make more money and where they need to be careful to avoid unknown risks.
There are four popular methods that are used when working with Machine Learning: supervised, unsupervised, semi-supervised, and reinforcement learning.
Contents:
Introduction
Chapter 1: Machine Learning
Chapter 2: Top Problems in Machine Learning
Chapter 3: Problems with Machine Learning That Have Not Been Solved
Chapter 4: Problems That Can Be Solved with Machine Learning
Chapter 5: Examples of Machine Learning
Chapter 6: Free Datasets
Chapter 7: Support Vector Algorithms
Chapter 8: Machine Learning with Python
Chapter 9: Keras and Machine Learning
Chapter 10: Theano and Machine Learning
Chapter 11: Neural Networks and Deep Learning
Conclusion
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