Python Machine Learning

Howey, Kevin

ISBN 10: 1914015231 / ISBN 13: 9781914015236

Edité par Digital Marketing Revolution ltd, 2020

A propos de cet article

182 pages. 7.99x5.00x0.39 inches. In Stock.

N° de réf. du vendeur 1914015231

Détails bibliographiques

• Titre: Python Machine Learning
• Éditeur: Digital Marketing Revolution ltd
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 1914015231
• ISBN à 13 chiffres: 9781914015236
• Reliure: Paperback
• État de l'article: Brand New

A propos de ce titre

Synopsis

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. ...and much, much more!


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.