Python 3 and Machine Learning Using ChatGPT /GPT-4

Front Cover....1

Half-Title Page....2

LICENSE, DISCLAIMER OF LIABILITY, AND LIMITED WARRANTY....3

Title Page....4

Copyright Page....5

Contents....8

Preface....18

Chapter 1: Introduction to Pandas....20

What is Pandas?....20

Pandas Options and Settings....21

Pandas Data Frames....21

Data Frames and Data Cleaning Tasks....22

Alternatives to Pandas....22

A Pandas Data Frame with a NumPy Example....23

Describing a Pandas Data Frame....25

Pandas Boolean Data Frames....27

Transposing a Pandas Data Frame....28

Pandas Data Frames and Random Numbers....28

Reading CSV Files in Pandas....30

Specifying a Separator and Column Sets in Text Files....31

Specifying an Index in Text Files....31

The loc() and iloc() Methods in Pandas....31

Converting Categorical Data to Numeric Data....32

Matching and Splitting Strings in Pandas....35

Converting Strings to Dates in Pandas....37

Working with Date Ranges in Pandas....39

Detecting Missing Dates in Pandas....40

Interpolating Missing Dates in Pandas....41

Other Operations with Dates in Pandas....43

Merging and Splitting Columns in Pandas....47

Reading HTML Web Pages in Pandas....49

Saving a Pandas Data Frame as an HTML Web Page....50

Summary....52

Chapter 2: Introduction to Machine Learning....54

What is Machine Learning?....54

Types of Machine Learning....55

Types of Machine Learning Algorithms....56

Machine Learning Tasks....58

Feature Engineering, Selection, and Extraction....59

Dimensionality Reduction....60

PCA....61

Covariance Matrix....62

Working with Datasets....62

Training Data Versus Test Data....62

What is Cross-validation?....63

What is Regularization?....63

Machine Learning and Feature Scaling....63

Data Normalization versus Standardization....64

The Bias-Variance Tradeoff....64

Metrics for Measuring Models....64

Limitations of R-Squared....65

Confusion Matrix....65

Accuracy versus Precision versus Recall....65

The ROC Curve....66

Other Useful Statistical Terms....66

What is an F1 score?....67

What is a p-value?....67

What is Linear Regression?....67

Linear Regression vs. Curve-Fitting....68

When are Solutions Exact Values?....68

What is Multivariate Analysis?....69

Other Types of Regression....69

Working with Lines in the Plane (optional)....70

Scatter Plots with NumPy and Matplotlib (1)....73

Why the Perturbation Technique is Useful....74

Scatter Plots with NumPy and Matplotlib (2)....75

A Quadratic Scatter Plot with NumPy and Matplotlib....75

The Mean Squared Error (MSE) Formula....77

A List of Error Types....77

Non-linear Least Squares....77

Calculating the MSE Manually....78

Approximating Linear Data with np.linspace()....79

Calculating MSE with np.linspace() API....80

Summary....82

Chapter 3: Classifiers in Machine Learning....84

What is Classification?....85

What are Classifiers?....85

Common Classifiers....85

Binary versus Multiclass Classification....86

Multilabel Classification....86

What are Linear Classifiers?....87

What is kNN?....87

How to Handle a Tie in kNN....87

What are Decision Trees?....88

What are Random Forests?....92

What are SVMs?....92

Tradeoffs of SVMs....93

What is Bayesian Inference?....93

Bayes’ Theorem....93

Some Bayesian Terminology....94

What is MAP?....94

Why Use Bayes’ Theorem?....95

What is a Bayesian Classifier?....95

Types of Naïve Bayes’ Classifiers....95

Training Classifiers....96

Evaluating Classifiers....96

What are Activation Functions?....97

Why Do We Need Activation Functions?....98

How Do Activation Functions Work?....98

Common Activation Functions....99

Activation Functions in Python....100

The ReLU and ELU Activation Functions....100

The Advantages and Disadvantages of ReLU....100

ELU....101

Sigmoid, Softmax, and Hardmax Similarities....101

Softmax....101

Softplus....101

Tanh....102

Sigmoid, Softmax, and HardMax Differences....102

What is Logistic Regression?....102

Setting a Threshold Value....103

Logistic Regression: Important Assumptions....103

Linearly Separable Data....104

Summary....104

Chapter 4: ChatGPT and GPT-4....106

What is Generative AI?....106

Important Features of Generative AI....106

Popular Techniques in Generative AI....107

What Makes Generative AI Unique....107

Conversational AI versus Generative AI....108

Primary Objectives....108

Applications....108

Technologies Used....109

Training and Interaction....109

Evaluation....109

Data Requirements....109

Is DALL-E Part of Generative AI?....109

Are ChatGPT and GPT-4 Part of Generative AI?....110

DeepMind....111

DeepMind and Games....111

Player of Games (PoG)....112

OpenAI....112

Cohere....113

Hugging Face....113

Hugging Face Libraries....113

Hugging Face Model Hub....114

AI21....114

InflectionAI....114

Anthropic....115

What is Prompt Engineering?....115

Prompts and Completions....116

Types of Prompts....116

Instruction Prompts....117

Reverse Prompts....117

System Prompts versus Agent Prompts....117

Prompt Templates....118

Prompts for Different LLMs....119

Poorly Worded Prompts....120

What is ChatGPT?....121

ChatGPT....121

ChatGPT: Google “Code Red”....122

ChatGPT versus Google Search....122

ChatGPT Custom Instructions....123

ChatGPT on Mobile Devices and Browsers....123

ChatGPT and Prompts....124

GPTBot....124

ChatGPT Playground....125

Plugins, Advanced Data Analysis, and Code Whisperer....125

Plugins....126

Advanced Data Analysis....127

Advanced Data Analysis Versus Claude 2....127

Code Whisperer....128

Detecting Generated Text....128

Concerns about ChatGPT....129

Code Generation and Dangerous Topics....129

ChatGPT Strengths and Weaknesses....130

Sample Queries and Responses from ChatGPT....131

Alternatives to ChatGPT....133

Google Gemini....133

YouChat....134

Pi from Inflection....134

Machine Learning and ChatGPT: Advanced Data Analysis....134

What is InstructGPT?....136

VizGPT and Data Visualization....136

What is GPT-4?....139

GPT-4 and Test-Taking Scores....139

GPT-4 Parameters....140

GPT-4 Fine Tuning....140

ChatGPT and GPT-4 Competitors....140

Gemini....141

CoPilot (OpenAI/Microsoft)....141

Codex (OpenAI)....142

Apple GPT....142

PaLM-2....143

Med-PaLM M....143

Claude 2....143

Llama 2....143

How to Download Llama 2....144

Llama 2 Architecture Features....144

Fine Tuning Llama 2....145

When Will GPT-5 Be Available?....145

Summary....146

Chapter 5: Linear Regression with GPT-4....148

What is Linear Regression?....149

Examples of Linear Regression....149

Metrics for Linear Regression....150

Coefficient of Determination (R^2)....151

Linear Regression with Random Data with GPT-4....152

Linear Regression with a Dataset with GPT-4....156

Descriptions of the Features of the death.csv Dataset....157

The Preparation Process of the Dataset....158

The Exploratory Analysis....160

Detailed EDA on the death.csv Dataset....162

Bivariate and Multivariate Analyses....165

The Model Selection Process....167

Code for Linear Regression with the death.csv Dataset....169

Describe the Model Diagnostics....172

Describe Additional Model Diagnostics....174

More Recommendations from GPT-4....175

Summary....176

Chapter 6: Machine Learning Classifiers with GPT-4....178

Machine Learning (According to GPT-4)....178

What is Scikit-Learn?....180

What is the kNN Algorithm?....182

Selecting the Value of k in the kNN Algorithm....183

Cross-Validation....183

Bias-Variance Tradeoff....184

Distance Metric....184

Square Root Rule....184

Domain Knowledge....184

Even versus Odd k....184

Computational Efficiency....184

Diversity in the Dataset....184

The Elbow Method for the kNN Algorithm....184

A Machine Learning Model with the kNN Algorithm....185

A Machine Learning Model with the Decision Tree Algorithm....191

A Machine Learning Model with the Random Forest Algorithm....196

A Machine Learning Model with the SVM Algorithm....201

The Logistic Regression Algorithm....204

The Naïve Bayes Algorithm....205

The SVM Algorithm....207

The Decision Tree Algorithm....208

The Random Forest Algorithm....210

Summary....212

Chapter 7: Machine Learning Clustering with GPT-4....214

What is Clustering?....214

Ten Clustering Algorithms....216

Metrics for Clustering Algorithms....219

K-means Clustering....222

Hierarchical Clustering....222

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)....223

What is the K-means Algorithm?....224

What is the Hierarchical Clustering Algorithm?....225

What is the DBSCAN Algorithm?....227

A Machine Learning Model with the K-means Algorithm....228

A Machine Learning Model with the Hierarchical Clustering Algorithm....232

A Machine Learning Model with the DBSCAN Algorithm....234

Summary....238

Chapter 8: ChatGPT and Data Visualization....240

Working with Charts and Graphs....240

Bar Charts....241

Pie Charts....241

Line Graphs....242

Heat Maps....242

Histograms....242

Box Plots....243

Pareto Charts....243

Radar Charts....243

Treemaps....244

Waterfall Charts....244

Line Plots with Matplotlib....244

Pie Charts Using Matplotlib....246

Box and Whisker Plots Using Matplotlib....247

Time Series Visualization with Matplotlib....248

Stacked Bar Charts with Matplotlib....249

Donut Charts Using Matplotlib....250

3D Surface Plots with Matplotlib....251

Radial (or Spider) Charts with Matplotlib....252

Matplotlib’s Contour Plots....254

Streamplots for Vector Fields....255

Quiver Plots for Vector Fields....257

Polar Plots....258

Bar Charts with Seaborn....259

Scatter Plots with Regression Lines Using Seaborn....260

Heatmaps for Correlation Matrices with Seaborn....261

Histograms with Seaborn....263

Violin Plots with Seaborn....264

Pair Plots Using Seaborn....265

Facet Grids with Seaborn....266

Hierarchical Clustering....267

Swarm Plots....268

Joint Plots for Bivariate Data....269

Point Plots for Factorized Views....270

Seaborn’s KDE Plots for Density Estimations....271

Seaborn’s Ridge Plots....273

Summary....275

Index....276

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.

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
• Features material on AI apps, GANs, and DALL-E
• Companion files with source code, datasets, and figures (available for downloading with Amazon proof of purchase by writing to the publisher at info@merclearning.com)