Python Machine Learning

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Python Machine Learning

Python Machine Learning: A Complete Guide to Machine Learning and Deep Learning with Python for Beginners (2022 Crash Course) by Nelson Holden
English | 2022 | ISBN: N/A | ASIN: B09ZYQ785F | 80 pages | EPUB | 0.59 Mb

Continue reading if you want to learn how to rapidly and simply create and master various Machine Learning algorithms.
We are now living in the age of Artificial Intelligence. Self-driving vehicles, personalized product suggestions, real-time pricing, voice and face recognition are just a few instances that demonstrate this point.
Consider medical diagnostics or the automation of boring and repetitive labor duties; these demonstrate that we live in exciting times.
A lot is going on in Machine Learning, from academic subjects to projects and applications in various phases of development.
Machines and automation play an important role in our everyday lives. Artificial intelligence is now one of the most flourishing sectors in which every programmer would want to work, and for a good reason: this is the future!
Machine Learning is the process of training computers to think and make choices in the same way that humans do. The distinction between how robots and humans learn is that humans learn by experiences, while machines learn from data.

Starting from the ground up, Python Machine Learning shows how this occurs, how computers gain experience, and how knowledge is compounded. Data is at the heart of Machine Learning because it contains realities beyond human comprehension.
The calculations that computers can do on data are remarkable, far beyond what the human brain is capable of. Once we have introduced data to a machine learning model, we must construct an environment in which the data stream is updated regularly. This improves the machine's learning capability. The more data Machine Learning models are exposed to, the simpler it is for these models to grow in power.

Inside, we'll talk about a variety of subjects, including:
  • What is Machine Learning, and how is it used in real-world situations?
  • Distinguishing between Machine Learning, Deep Learning, and Artificial Intelligence
  • Supervised learning, unsupervised learning, and semi-supervised learning.
  • Models for machine learning training
  • How to Use Python Lists and Modules
  • Python's 12 Must-Have Machine Learning Libraries
  • What exactly is the Tensorflow library?
  • Artificial Neural Networks (ANNs)

  • And a lot more!

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