Название: Python For Data Science: The Ultimate Beginners’ Guide to Learning Python Data Science Step by Step
Автор: Ethan Williams
Издательство: Independently published
Год: 2019
Страниц: 199
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

This book is a comprehensive guide for beginners to learn Python Programming, especially its application for Data Science. While the lessons in this book are targeted at the absolute beginner to programming, people at various levels of proficiency in Python, or any other programming languages can also learn some basics and concepts of data science. A few Python libraries are introduced, including NumPy, Pandas, Matplotlib, and Seaborn for data analysis and visualisation.

To make the lessons more intuitive and relatable, practical examples and applications of each lesson are given. The reader is equally encouraged to practise the techniques via exercises, within and at the end of the relevant chapters. To help the reader get a full learning experience, there are references to relevant reading and practice materials, and the reader is encouraged to click these links and explore the possibilities they offer. It is expected that with consistency in learning and practicing the reader can master Python and the basics of its application in data science. The only limitation to the reader’s progress, however, is themselves!

Machine learning is an advanced form of data analysis and computation that utilizes the exceptional processing speed and pattern recognition techniques of computers for finding and learning new trends in data. Quite long-winded huh? Simply put, it is an artificial-intelligence-inspired technique of programming that allows computers to improve their learning capabilities through the data they are fed, or can access. This is much like the way human beings develop through life.

As with most applications of Python, there is a library for Machine Learning and it’s called Scikit Learn. The Scikit learn package is most likely included in your distribution of Anaconda, and you can go ahead and verify with conda list at the anaconda prompt.

Contents:









[related-news] [/related-news]