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Machine Learning with Python

Complete Step-by-Step Guide for Beginners to Learning Machine Learning Technology, Principles, Application and the Importance It Has Today

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Machine Learning with Python

Auteur(s): David Park
Narrateur(s): Shane Makena
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À propos de cet audio

It is not necessary to work on the projects associated with your job profile; you can work overtime by working on some projects which are not related to your job profile but go perfectly with your skill sets. It would give a good impression to your boss, which would further lead to promotions. It might lead to a change in your role in the organization. This would lead you to the road map of your career in this field.

Python is a high-level scripting language. It is easy to learn and robust than other words because of its dynamic nature and simple syntax which allows small lines of code. Included indentation and object-oriented, functional programming make it simple. Such advantages of Python makes it different from another language, and that's why Python is preferred for development in companies mostly. In industries, machine learning using python has become popular. This is because it has standard libraries that are used for scientific and numerical calculations. Also, it can be operated on Linux, Windows, Mac OS, and UNIX.

What you will gain in this book:

  • What is meant by machine learning?
  • A short history of machine learning.
  • Machine learning - automation within a knowledge.
  • The challenges of machine learning.
  • Advantages and disadvantages of machine learning language.
  • Machine learning in robotics.
  • Machine learning applications.
  • Machine learning algorithms.
  • How machine learning is changing the world - and your everyday life.

Machine learning is a new trending field these days and is an application of artificial intelligence. The main aim of machine learning is to create intelligent machines that can think and work like human beings.

©2019 David Park (P)2020 David Park
Affaires et carrières Informatique Science des données
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