Название: Python 3 and Machine Learning Using ChatGPT / GPT-4
Автор: O. Campesato
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 286
Язык: английский
Формат: pdf (true), epub (true)
Размер: 21.8 MB, 30.3 MB

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.

Each chapter is crafted to build on the knowledge from the previous sections, ensuring a cohesive and comprehensive learning experience. To cater to a wide range of learning styles, the book includes step-by-step tutorials, real-world applications, and sections dedicated to theoretical concepts backed by practical examples. This approach not only solidifies understanding but also enhances your ability to apply these techniques in real-world scenarios.

The Chapter 1 introduces you to Pandas and provides code samples that illustrate some of its useful features. If you are familiar with these topics, skim through the material and peruse the code samples, just in case they contain information that is new to you. The first part contains a brief introduction to Pandas. This section contains code samples that illustrate some features of Pandas DataFrames and a brief discussion of series, which are two of the main features of Pandas. The second part of this chapter discusses various types of data frames that you can create, such as numeric and Boolean data frames. In addition, we discuss examples of creating data frames with NumPy functions and random numbers.

The Chapter 2 introduces numerous concepts in machine learning, such as feature selection, feature engineering, data cleaning, training sets, and test sets. The first part of this chapter briefly discusses machine learning and the sequence of steps that are typically required to prepare a dataset. These steps include “feature selection” or “feature extraction” that can be performed using various algorithms. The second section describes the types of data that you can encounter, issues that can arise with the data in datasets, and how to rectify them. You will also learn about the difference between “hold out” and “k-fold” when you perform the training step. The third part of this chapter briefly discusses the basic concepts involved in linear regression.

Features:
Includes practical tutorials designed to provide hands-on experience, reinforcing learning through practice
Provides coverage of the latest Python tools using state-of-the-art libraries essential for modern data scientists
Companion files with source code, datasets, and figures are available for downloading

Contents:












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