Python 3 and Machine Learning Using ChatGPT/GPT-4

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.

Saved in:
Bibliographic Details
Main Author Campesato, Oswald
Format eBook
LanguageEnglish
Published Herndon, VA Mercury Learning & Information 2024
Mercury Learning and Information
Edition1
SeriesMLI Generative AI Series
Subjects
ChatGPT-4
Classifiers
Computer programming
COMPUTERS / Programming / Open Source
Data Science
Data Visualization
Linear Regression
Machine Learning
Pandas
Programming
Python
Python (Computer program language)
Python, Machine Learning, ChatGPT-4, Pandas, Classifiers, Linear Regression, Data Visualization, AI, Data Science, Programming
Video games-Programming
Data Science
Classifiers
Python, Machine Learning, ChatGPT-4, Pandas, Classifiers, Linear Regression, Data Visualization, AI, Data Science, Programming
Linear Regression
Pandas
AI
Programming
Data Visualization
ChatGPT-4
Machine Learning
Python
Online AccessGet full text
ISBN9781501522956
1501522957
DOI10.1515/9781501520112

Cover

Table of Contents:
  • 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
  • Half-Title Page -- LICENSE, DISCLAIMER OF LIABILITY, AND LIMITED WARRANTY -- Title Page -- Copyright Page -- 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
  • Chapter 2: Introduction to Machine Learning --
  • Contents --
  • Chapter 1: Introduction to Pandas --
  • Chapter 6: Machine Learning Classifiers with GPT-4 --
  • Chapter 7: Machine Learning Clustering with GPT-4 --
  • Chapter 8: ChatGPT and Data Visualization --
  • Preface --
  • Chapter 3: Classifiers in Machine Learning --
  • Frontmatter --
  • Index
  • Chapter 4: ChatGPT and GPT-4 --
  • Chapter 5: Linear Regression with GPT-4 --