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