Machine Learning with Python for Everyone

Download Machine Learning with Python for Everyone written by Mark E. Fenner in PDF format. This book is under the category Computers and bearing the isbn/isbn13 number 0134845625; 0134845641/9780134845623/9780134845647. You may reffer the table below for additional details of the book.


SKU: 4a8423d5e91f Category: Tags: ,



Mark E. Fenner


Addison-Wesley Professional




592 pages






0134845625; 0134845641



Book Description

The Complete and Step-by-Step Introduction for Novices to Understand and Construct Machine Learning Systems Using Python

Even if you are starting from square one, the Machine Learning with Python for Everyone (PDF) book will walk you through the steps necessary to master the patterns, processes, and strategies you’ll need to build useful learning systems. This ebook is for you if you are able to write Python code, regardless of how much or how little math you know at the college level. Mark E. Fenner, the primary instructor, communicates the concepts of machine learning through the use of stories written in plain English, pictures, and examples written in Python.

Mark will begin by discussing machine learning and what it is capable of, introducing important mathematical and computational topics in an approachable manner, and guiding you through the first steps in constructing, training, and evaluating learning systems. You will, step by step, complete the components of a practical learning system, expand your toolbox, and learn some of the field’s most cutting-edge and exciting techniques. Whether you are an analyst, a college student, a scientist, or a hobbyist, the perceptions that are presented in this guide will be applicable to every learning system that you ever construct or use.



  • Apply methods of machine learning to both the pictures and the text.


  • Establish connections between the fundamental ideas and the neural networks and graphical models.


  • Utilize the Python sci-kit-learn library in addition to a variety of other potent tools.


  • Utilize feature engineering in order to transform rough data into useful forms.


  • Evaluate the effectiveness of machine learning systems using a realistic benchmark.


  • Integrate a number of separate parts into a single system and adjust the settings accordingly.


  • Use the classifiers to organize the examples, and use the regressors to quantify the examples.


  • Gain familiarity with the fundamental machine learning concepts, as well as machine learning algorithms and models.



PLEASE TAKE NOTE That the product only includes the PDF version of the ebook titled “Machine Learning with Python for Everyone.” There are no access codes contained within.



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