• Machine Learning with Python: The Absolute Guide for Beginner's and Faster Way to Learn Python Machine Learning

  • Intermediate, Advanced, to Expert Concepts
  • By: Steven Samelson
  • Narrated by: Kevin Tromp
  • Length: 5 hrs and 56 mins
  • 3.0 out of 5 stars (1 rating)

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Publisher's Summary

Machine Learning:

The Complete Beginner’s Guide to learn and Understand Machine Learning, gives you insights into what machine learning entails and how it can impact the way you can weaponize data to gain incredible insights. Your information is pretty much as good as what you are doing with it and the way you manage it. In this book, you find out types of machine learning techniques, models, and algorithms that can help achieve results for your company.     

Python Programming:

You want to learn the language quickly to be productive when you use it to do your real work, which could be anything. Unlike most books on the subject, it starts from the beginning showing you t differences between Python and other languages. As a result, you get an understanding of what needs to be done from the beginning, using practical examples and spending a lot of time doing really useful tasks. You can also get help installing Python on your particular system.    

It is essential to understand how each example works before moving on to the next one. Each example has lessons that are important to you, and you may lose important content if you start to miss too much information.    

In this 2-in-1 book, you will find:    

  • Simplicity: It is written in a very concise and approachable way  
  • Coaching: All instructions are presented in a linear, step-by-step format  
  • Detail: Its scope is to comprehensively address both novices and expert  
  • Structure: Information is clearly organized into chapters and sections  
  • Two Books: A Python crash course and a Python data analysis course -How we classify machine learning algorithms  
  • How we can apply machine learning in different areas  
  • Understanding the artificial neural networks  
  • The use of convoluted neural networks  
  • Building predictive models  
  • Autoencoders in ML and Python  
  • K-Means techniques and Natural Language Processing  
  • The art of feature engineering  
  • The ensemble methods
©2020 Setven Samelson (P)2020 Setven Samelson

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