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  • Machine Learning with Python

  • 2 Manuscript in 1: Complete Guide to Learning Machine Learning & What You Need to Know About Data Science
  • By: David Park
  • Narrated by: Matthew Kinsey, Shane Makena
  • Length: 6 hrs and 42 mins
  • 5.0 out of 5 stars (15 ratings)

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Machine Learning with Python

By: David Park
Narrated by: Matthew Kinsey, Shane Makena
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Publisher's summary

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 lead to having a good impression on 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 more robust than other languages because of its dynamic nature and simple syntax, which allows small lines of code. 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.

©2020 David Park (P)2020 David Park
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What listeners say about Machine Learning with Python

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Best audio book

This is the best audio book you can buy if you want to implement machine learning to your project.

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Highly recommended!

This audio book helped me immensely with understanding and using a lot of different kinds of machine learning models. Highly recommended!

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Easy to follow

The examples in the audio book are really good and easy to follow. I have been using this as a reference for my project. It’s worth every penny!

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Very practical

Wonderful audio book! Just what I expected. Very practical, hands-on like the title says. I have the first edition and I don’t regret one single bit buying the second. A must for any machine learning practitioner!

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The most complete python based ML audio book

A classic. Clearly demarcated to sklearn based non-deep learning ML section and the deep learning portion which goes in-depth into Keras and tf. A good amount of material on deep reinforcement learning as well.

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I would recommend this audio book

The audio book is meant more for practitioners but there is still enough math and lots of references to papers if the listener wants more theoretical information. I would recommend this audio book to anyone whether you are an undergrad or a PhD with several years of experience. Truly a well written audio book !

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Like it!

What I really like about this is a audio book is that the author knows how to explain ML concepts by solving a real-world example problem, as opposed to just explaining the theory.

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Good

Excellent audio book to get into the most popular libraries for machine learning using python. Good examples and support.

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Detailed

I'm very pleased with this audio book. I enjoy the little bits of humor here and there, and it does a great job not glossing over important details that might be a stumbling block for someone.

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Choose it for ML

If you want a audio book to illustrate ML algorithms with TF implementation, you can choose this one.

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