Python 3 For Machine Learning
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Python Programming, Deep Learning
Easily Boost Your Skills In Python Programming & Become A Master In Deep Learning & Data Analysis! 💻 Python is an interpreted, high-level, general-purpose programming language that emphasizes code readability with its notable use of significant whitespace. What makes Python so popular in the IT industry is that it uses an object-oriented approach, which enables programmers to write clear, logical code for all types of projects, whether big or small. Hone your Python Programming skills and gain a sharp edge over other programmers the EASIEST way possible... with this practical beginner’s guide! In his 3-in-1 Python crash course for beginners, Anthony Adams gives novices like you simple, yet efficient tips and tricks to become a MASTER in Python coding for artificial intelligence, neural networks, machine learning, and data science/analysis! Here’s what you’ll get: ✅ Highly innovative ways to boost your understanding of Python programming, data analysis, and machine learning ✅ Quickly and effectively stop fraud with machine learning ✅ Practical and efficient exercises that make understanding Python quick & easy And so much more! As a beginner, you might feel a bit intimidated by the complexities of coding. Add the fact that most Python Programming crash course guides make learning harder than it has to be! ✓ With the help of this 3-in-1 guide, you will be given carefully sequenced Python Programming lessons that’ll maximize your understanding, and equip you with all the skills for real-life application! ★ Thrive in the IT industry with this comprehensive Python Programming crash course! ★ Scroll up, Click on “Buy Now”, and Start Learning Today!
Python 3 and Machine Learning Using ChatGPT/GPT-4
Author: Oswald Campesato
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2024-06-17
This book is designed to bridge the gap between theoretical knowledge and practical application in the fields of Python programming, machine learning, and the innovative use of ChatGPT-4 in data science. The book is structured to facilitate a deep understanding of several core topics. It begins with a detailed introduction to Pandas, a cornerstone Python library for data manipulation and analysis. Next, it explores a variety of machine learning classifiers from kNN to SVMs. In later chapters, it discusses the capabilities of GPT-4, and how its application enhances traditional linear regression analysis. Finally, the book covers the innovative use of ChatGPT in data visualization. This segment focuses on how AI can transform data into compelling visual stories, making complex results accessible and understandable. It includes material on AI apps, GANs, and DALL-E. Companion files are available for downloading with code and figures from the text.
Python
THIS BOOK INCLUDES : Python for Beginners: A crash course to learn Python Programming in 1 Week Python for Data Analysis: A Beginners Guide to Master the Fundamentals of Data Science and Data Analysis by Using Pandas, Numpy and Ipython Python Machine Learning: A Step by Step Beginner’s Guide to Learn Machine Learning Using Python Here's what you'll learn through this book: Python for Beginners In this book You will learn: Getting started with the basics Statements, Comments, Variables, Index Data Types: Strings and Numbers Data Types: List and Tuple Data Types: Set and Dictionary Operators Functions Loops Python Practice Projects and much more Python for Data Analysis In this book You will learn: Data Science/Analysis and its applications IPython and Jupyter - an introduction to the basic tools and how to navigate and use them. You will also learn about its importance in a data scientist’s ecosystem. Pandas - a powerful data management Python library that lets you do interesting things with data. You will learn all the basics you need to get started. NumPy - a powerful numerical library for Python. You will learn more about its advantages. Python Machine Learning The Topics Covered Include: Machine learning fundamentals How to set up the development environment How to use Python libraries and modules like Scikit-learn, TensorFlow, Matplotlib, and NumPy How to explore data How to solve regression and classification problems Decision trees k-means clustering Feed-forward and recurrent neural networks Get your copy now!