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Mastering Machine Learning with Python By Manohar Swamynathan

Mastering Machine Learning with Python
Mastering Machine Learning with Python

About The E-Book

  • Title – Master Machine Learning with Python in Six Steps_ A Practical Implementation Guide to Predictive Data Analytics Using Python
  • Author – Manohar Swamynathan
  • Total Pages – 374 Pages
  • Total Chapters – 7 Chapters
  • E-Book Size – 09 MBs

Explore fundamental to advanced topics to mastering machine learning with Python in six steps. This will help you become a competent practitioner.

The approach taken in this book is based on the “Six Degrees of Separation” theory, which holds that everything and everyone is only six steps apart. Each topic is divided into two parts in Mastering Machine Learning with Python in Six Steps: theoretical concepts and practical implementation using appropriate Python packages.

Book Contents

  • Python 3.6 (or 2.7)
  • SciPy
  • NumPy
  • Matplotlib
  • Pandas
  • Scikit-Learn

Mastering Machine Learning – Example Pages

Mastering Machine Learning Ex 1
Mastering Machine Learning Ex 2
Mastering Machine Learning Ex 3

Download “Mastering-Machine-Learning-with-Python-in-Six-Steps_-A-Practical-Implementation-Guide-to-Predictive-Data-Analytics-Using-Python” Mastering-Machine-Learning-with-Python-in-Six-Steps_-A-Practical-Implementation-Guide-to-Predictive-Data-Analytics-Using-Python (Mr Techies).pdf – Downloaded 6 times – 9.60 MB

After reading this ebook, you will know…

  • How to deliver a model that can make accurate predictions on new unseen data.
  • How to complete all subtasks of a predictive modeling problem with Python.
  • How to learn new and different techniques in Python and SciPy.
  • How to work through a small to medium-sized dataset end-to-end.
  • How to get help with Python machine learning.

You will know which Python modules, classes, and functions to use for everyday machine-learning tasks.

From here, you can dive into the specifics of the functions, techniques, and algorithms used to learn how to use them better to deliver more accurate predictive models in less time.

Thank You !!

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