Python 3 and Machine Learning Using ChatGPT / GPT-4

✍ Scribed by Oswald Campesato

• Publisher: Mercury Learning And Information
• Year: 2024
• Tongue: English
• Leaves: 434
• Category: Library

✦ Synopsis

he 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
Companion files with source code, datasets, and figures are available for downloading

✦ Table of Contents

Front Cover
Half-Title Page
Title Page
Copyright Page
Dedication
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