Python Machine Learning: The Complete Beginners Guide to Programming and Deep Learning, Data Science and Artificial Intelligence Using Scikit-L

Howey, Kevin

  • 出版商: Independently Published
  • 出版日期: 2019-07-28
  • 售價: $890
  • 貴賓價: 9.5$846
  • 語言: 英文
  • 頁數: 182
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1082753548
  • ISBN-13: 9781082753541
  • 相關分類: Python程式語言人工智慧Machine LearningDeepLearningData Science
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The world of technology is growing all the time.
It seems like a new technology is coming out all of the time, and it seems like it is outpacing what most traditional coding languages are able to do.

While there is a lot that you are able to with traditional forms of coding, it isn't able to meet all of your needs. What if we were able to make a program that was able to learn on its own? What if we could put in a bit of information, and the program were able to do what it needed to, and take control. This is where the beauty of machine learning is going to come into play

This guidebook is going to take some time to look at machine learning, and how you are able to work with the Python language in order to make it work and to help you create some of the best programs out there

Imagine all that you can do when you bring in machine learning and can create programs and more that can think for and learn on their own

Some of the topics that we are going to explore with Python machine learning inside this guidebook include:

  • The different types of machine learning that you are able to work with.

  • The difference between machine learning and deep learning.

  • How to set up and use the Scikit-learn library from Python.

  • How to set up and use the TensorFlow library.

  • The K-Nearest Neighbors and the K-Means clustering algorithms.

  • How to use support vector machines with machine learning.

  • Working with neural networks and recurrent neural networks.

  • How decisions trees can help you make smarter decisions, and turning these decision trees into random forests.

  • Working with linear classifiers when you are in machine learning.

There are so many things that we are able to work with when it comes to machine learning, and the field is going to grow in leap and bounds through the years.

If you are ready to learn more about machine learning and how to implement some of the algorithms with the help of Python, make sure to check out this guidebook to get started.

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