PYTHON PROGRAMMING INTERMEDIATE: The Ultimate Guide to Boosting Your Career Through Intermediate Python and Machine Learning Computer Procedures (Crash Course Tips And Tricks Book 2)
Book Details
Author(s)LEWIS TAYLOR
ISBN / ASINB07YZMF5R4
ISBN-13978B07YZMF5R6
Sales Rank40,976
MarketplaceUnited States 🇺🇸
Description
Have you started a journey on this Python language developing program but long to learn further and improve your skills? Are you looking to take your python abilities to the next level?
Then you’ve come to the right place for this book on Intermediate Python Programming is just the book you need!
Python is now one of the most significant and influential languages in the IT globe. It is an important and very strong programming language that has a big number of areas of implementation that shape our future.
Programming is practical, but the theory is also essential, nevertheless it can be kept simple and accurate.
This is exactly what you will find in this book; important theory explained precisely, backed up with lots of practical code and at the same time you can finish this book in a couple of days because we don't beat around the bush!
You will learn sophisticated language ideas in this second volume of the Python guidebook by building on the basics already accomplished.
You will be able to create state-of-the-art and complicated apps, comprehend sophisticated programming paradigms that will assist you to learn not only Python, but also other languages such as Java or C++ and thus create an incredible foundation for your future career in programming.
In this Python guide, you will discover;
What machine learning is and why a programmer would want to learn how to use it
Some of the basics of coding with Python and how to read the codes that we will work on
The Reasons that many programmers are flocking to this coding language and eager to learn more
Learning some of the building blocks that will ensure your success with machine learning
How to set up the right environment in Python and get the libraries set up
How K-Means clustering is going to be different from KNN
How to work with statistics and probability in order to understand more about machine learning
What the generators are all about and how to use them to add some more strength to your own codes
The difference between supervised, unsupervised and reinforcement learning
And much more!
