Python 3 and Machine Learning Using ChatGPT / GPT-4 E-Book

Mercury Learning and Information

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 338

Veröffentlichungsjahr: 2024

Beschreibung

This book bridges the gap between theoretical knowledge and practical application in Python programming, machine learning, and using ChatGPT-4 in data science. It starts with an introduction to Pandas for data manipulation and analysis. The book then explores various machine learning classifiers, from kNN to SVMs. Later chapters cover GPT-4's capabilities, enhancing linear regression analysis, and using ChatGPT in data visualization, including AI apps, GANs, and DALL-E.
The journey begins with mastering Pandas and machine learning fundamentals. It progresses to applying GPT-4 in linear regression and machine learning classifiers. The final chapters focus on using ChatGPT for data visualization, making complex results accessible and understandable.
Understanding these concepts is crucial for modern data scientists. This book transitions readers from basic Python programming to advanced applications of ChatGPT-4 in data science. Companion files with source code, datasets, and figures enhance learning, making this an essential resource for mastering Python, machine learning, and AI-driven data visualization.

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O. Campesato. Python 3 and Machine Learning Using ChatGPT / GPT-4.

ISBN: 978-1-50152-295-6

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I’d like to dedicate this book to my parents

– may this bring joy and happiness into their lives.

CONTENTS

Preface

Chapter 1: Introduction to Pandas

What is Pandas?

Pandas Options and Settings

Pandas Data Frames

Data Frames and Data Cleaning Tasks

Alternatives to Pandas

A Pandas Data Frame with a NumPy Example

Describing a Pandas Data Frame

Pandas Boolean Data Frames

Transposing a Pandas Data Frame

Pandas Data Frames and Random Numbers

Reading CSV Files in Pandas

Specifying a Separator and Column Sets in Text Files

Specifying an Index in Text Files

The loc() and iloc() Methods in Pandas

Converting Categorical Data to Numeric Data

Matching and Splitting Strings in Pandas

Converting Strings to Dates in Pandas

Working with Date Ranges in Pandas

Detecting Missing Dates in Pandas

Interpolating Missing Dates in Pandas

Other Operations with Dates in Pandas

Merging and Splitting Columns in Pandas

Reading HTML Web Pages in Pandas

Saving a Pandas Data Frame as an HTML Web Page

Summary

Chapter 2: Introduction to Machine Learning

What is Machine Learning?

Types of Machine Learning

Types of Machine Learning Algorithms

Machine Learning Tasks

Feature Engineering, Selection, and Extraction

Dimensionality Reduction

PCA

Covariance Matrix

Working with Datasets

Training Data Versus Test Data

What is Cross-validation?

What is Regularization?

Machine Learning and Feature Scaling

Data Normalization versus Standardization

The Bias-Variance Tradeoff

Metrics for Measuring Models

Limitations of R-Squared

Confusion Matrix

Accuracy versus Precision versus Recall

The ROC Curve

Other Useful Statistical Terms

What is an F1 score?

What is a p-value?

What is Linear Regression?

Linear Regression vs. Curve-Fitting

When are Solutions Exact Values?

What is Multivariate Analysis?

Other Types of Regression

Working with Lines in the Plane (optional)

Scatter Plots with NumPy and Matplotlib (1)

Why the Perturbation Technique is Useful

Scatter Plots with NumPy and Matplotlib (2)

A Quadratic Scatter Plot with NumPy and Matplotlib

The Mean Squared Error (MSE) Formula

A List of Error Types

Non-linear Least Squares

Calculating the MSE Manually

Approximating Linear Data with np.linspace()

Calculating MSE with np.linspace() API

Summary

Chapter 3: Classifiers in Machine Learning

What is Classification?

What are Classifiers?

Common Classifiers

Binary versus Multiclass Classification

Multilabel Classification

What are Linear Classifiers?

What is kNN?

How to Handle a Tie in kNN

What are Decision Trees?

What are Random Forests?

What are SVMs?

Tradeoffs of SVMs

What is Bayesian Inference?

Bayes’ Theorem

Some Bayesian Terminology

What is MAP?

Why Use Bayes’ Theorem?

What is a Bayesian Classifier?

Types of Naïve Bayes’ Classifiers

Training Classifiers

Evaluating Classifiers

What are Activation Functions?

Why Do We Need Activation Functions?

How Do Activation Functions Work?

Common Activation Functions

Activation Functions in Python

The ReLU and ELU Activation Functions

The Advantages and Disadvantages of ReLU

ELU

Sigmoid, Softmax, and Hardmax Similarities

Softmax

Softplus

Tanh

Sigmoid, Softmax, and HardMax Differences

What is Logistic Regression?

Setting a Threshold Value

Logistic Regression: Important Assumptions

Linearly Separable Data

Summary

Chapter 4: ChatGPT and GPT-4

What is Generative AI?

Important Features of Generative AI

Popular Techniques in Generative AI

What Makes Generative AI Unique

Conversational AI versus Generative AI

Primary Objectives

Applications

Technologies Used

Training and Interaction

Evaluation

Data Requirements

Is DALL-E Part of Generative AI?

Are ChatGPT and GPT-4 Part of Generative AI?

DeepMind

DeepMind and Games

Player of Games (PoG)

OpenAI

Cohere

Hugging Face

Hugging Face Libraries

Hugging Face Model Hub

AI21

InflectionAI

Anthropic

What is Prompt Engineering?

Prompts and Completions

Types of Prompts

Instruction Prompts

Reverse Prompts

System Prompts versus Agent Prompts

Prompt Templates

Prompts for Different LLMs

Poorly Worded Prompts

What is ChatGPT?

ChatGPT

ChatGPT: Google “Code Red”

ChatGPT versus Google Search

ChatGPT Custom Instructions

ChatGPT on Mobile Devices and Browsers

ChatGPT and Prompts

GPTBot

ChatGPT Playground

Plugins, Advanced Data Analysis, and Code Whisperer

Plugins

Advanced Data Analysis

Advanced Data Analysis Versus Claude 2

Code Whisperer

Detecting Generated Text

Concerns about ChatGPT

Code Generation and Dangerous Topics

ChatGPT Strengths and Weaknesses

Sample Queries and Responses from ChatGPT

Alternatives to ChatGPT

Google Gemini

YouChat

Pi from Inflection

Machine Learning and ChatGPT: Advanced Data Analysis

What is InstructGPT?

VizGPT and Data Visualization

What is GPT-4?

GPT-4 and Test-Taking Scores

GPT-4 Parameters

GPT-4 Fine Tuning

ChatGPT and GPT-4 Competitors

Gemini

CoPilot (OpenAI/Microsoft)

Codex (OpenAI)

Apple GPT

PaLM-2

Med-PaLM M

Claude 2

Llama 2

How to Download Llama 2

Llama 2 Architecture Features

Fine Tuning Llama 2

When Will GPT-5 Be Available?

Summary

Chapter 5: Linear Regression with GPT-4

What is Linear Regression?

Examples of Linear Regression

Metrics for Linear Regression

Coefficient of Determination (R^2)

Linear Regression with Random Data with GPT-4

Linear Regression with a Dataset with GPT-4

Descriptions of the Features of the death.csv Dataset

The Preparation Process of the Dataset

The Exploratory Analysis

Detailed EDA on the death.csv Dataset

Bivariate and Multivariate Analyses

The Model Selection Process

Code for Linear Regression with the death.csv Dataset

Describe the Model Diagnostics

Describe Additional Model Diagnostics

More Recommendations from GPT-4

Summary

Chapter 6: Machine Learning Classifiers with GPT-4

Machine Learning (According to GPT-4)

What is Scikit-Learn?

What is the kNN Algorithm?

Selecting the Value of k in the kNN Algorithm

Cross-Validation

Bias-Variance Tradeoff

Distance Metric

Square Root Rule

Domain Knowledge

Even versus Odd k

Computational Efficiency

Diversity in the Dataset

The Elbow Method for the kNN Algorithm

A Machine Learning Model with the kNN Algorithm

A Machine Learning Model with the Decision Tree Algorithm

A Machine Learning Model with the Random Forest Algorithm

A Machine Learning Model with the SVM Algorithm

The Logistic Regression Algorithm

The Naïve Bayes Algorithm

The SVM Algorithm

The Decision Tree Algorithm

The Random Forest Algorithm

Summary

Chapter 7: Machine Learning Clustering with GPT-4

What is Clustering?

Ten Clustering Algorithms

Metrics for Clustering Algorithms

K-means Clustering

Hierarchical Clustering

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

What is the K-means Algorithm?

What is the Hierarchical Clustering Algorithm?

What is the DBSCAN Algorithm?

A Machine Learning Model with the K-means Algorithm

A Machine Learning Model with the Hierarchical Clustering Algorithm

A Machine Learning Model with the DBSCAN Algorithm

Summary

Chapter 8: ChatGPT and Data Visualization

Working with Charts and Graphs

Bar Charts

Pie Charts

Line Graphs

Heat Maps

Histograms

Box Plots

Pareto Charts

Radar Charts

Treemaps

Waterfall Charts

Line Plots with Matplotlib

Pie Charts Using Matplotlib

Box and Whisker Plots Using Matplotlib

Time Series Visualization with Matplotlib

Stacked Bar Charts with Matplotlib

Donut Charts Using Matplotlib

3D Surface Plots with Matplotlib

Radial (or Spider) Charts with Matplotlib

Matplotlib’s Contour Plots

Streamplots for Vector Fields

Quiver Plots for Vector Fields

Polar Plots

Bar Charts with Seaborn

Scatter Plots with Regression Lines Using Seaborn

Heatmaps for Correlation Matrices with Seaborn

Histograms with Seaborn

Violin Plots with Seaborn

Pair Plots Using Seaborn

Facet Grids with Seaborn

Hierarchical Clustering

Swarm Plots

Joint Plots for Bivariate Data

Point Plots for Factorized Views

Seaborn’s KDE Plots for Density Estimations

Seaborn’s Ridge Plots

Summary

Index

PREFACE

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 in data science. It aims to provide a comprehensive guide for those who aspire to deepen their understanding and enhance their skills in these rapidly evolving areas.

The motivation stems from a growing demand for practical, in-depth resources that cater to the needs of students, data scientists, and AI researchers looking to leverage advanced techniques and tools. As these fields continue to grow in importance and impact, the ability to adeptly manipulate data, understand machine learning algorithms, and apply the latest advancements in AI becomes critical.

This book is structured to facilitate a deep understanding of several core topics:

■ Introduction to Pandas: We begin with a detailed introduction to Pandas, a cornerstone Python library for data manipulation and analysis. This section is tailored to help you master data frames and perform complex data cleaning and preparation tasks efficiently.

■ Machine Learning Classifiers: Next, we explore a variety of machine learning classifiers, providing you with the knowledge to choose and implement the right algorithm for your projects. From kNN to SVMs, you will learn the intricacies of each method through practical examples.

■ GPT-4 and Linear Regression: As we explore the capabilities of GPT-4, we discuss its application in enhancing traditional linear regression analysis. This section demonstrates how GPT-4 can be used to perform and interpret regression in ways that push the boundaries of conventional data analysis.

■ Data Visualization with ChatGPT: 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 AI apps, GANs, and DALL-E.

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.

Features of This Book

■ Coverage of Latest Python Libraries: You will gain proficiency in using state-of-the-art libraries essential for modern data scientists.

■ Real-World Problem Solving: The book challenges you to apply your skills on real data, preparing you for professional success.

■ Companion files with source code, datasets, and figures are available for downloading by writing to the publisher (with proof of purchase) to [email protected].

This book is more than just a learning tool; it is a reference that you will return to repeatedly as you progress in your career. Whether you are a beginner aiming to get a solid start in programming and data science or an experienced professional looking to explore new advancements in AI, “Python 3 and Machine Learning Using ChatGPT/GPT-4” is an invaluable asset.

We hope that you will find this book to be a valuable resource, one that inspires you to explore further and apply your knowledge to solve complex problems. The future of Generative AI is exciting and full of possibilities.

O. Campesato

April 2024