book

Python 3 and Machine Learning Using ChatGPT/GPT-4

Name: Python 3 and Machine Learning Using ChatGPT/GPT-4
Author: Oswald Campesato
ISBN: 9781501520174

by Oswald Campesato

June 2024

268 pages

6h 40m

English

Mercury Learning and Information

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Front Cover
Half-Title Page
Title Page
Copyright Page
Dedication
Contents
Preface
Chapter 1: Introduction to Pandas
What is Pandas?Pandas Options and SettingsPandas Data FramesData Frames and Data Cleaning TasksAlternatives to PandasA Pandas Data Frame with a NumPy ExampleDescribing a Pandas Data FramePandas Boolean Data FramesTransposing a Pandas Data FramePandas Data Frames and Random NumbersReading CSV Files in PandasSpecifying a Separator and Column Sets in Text FilesSpecifying an Index in Text FilesThe loc() and iloc() Methods in PandasConverting Categorical Data to Numeric DataMatching and Splitting Strings in PandasConverting Strings to Dates in PandasWorking with Date Ranges in PandasDetecting Missing Dates in PandasInterpolating Missing Dates in PandasOther Operations with Dates in PandasMerging and Splitting Columns in PandasReading HTML Web Pages in PandasSaving a Pandas Data Frame as an HTML Web PageSummary
Chapter 2: Introduction to Machine Learning
What is Machine Learning?Types of Machine LearningTypes of Machine Learning AlgorithmsMachine Learning TasksFeature Engineering, Selection, and ExtractionDimensionality ReductionPCACovariance MatrixWorking with DatasetsTraining Data Versus Test DataWhat is Cross-validation?What is Regularization?Machine Learning and Feature ScalingData Normalization versus StandardizationThe Bias-Variance TradeoffMetrics for Measuring ModelsLimitations of R-SquaredConfusion MatrixAccuracy versus Precision versus RecallThe ROC CurveOther Useful Statistical TermsWhat is an F1 score?What is a p-value?What is Linear Regression?Linear Regression vs. Curve-FittingWhen are Solutions Exact Values?What is Multivariate Analysis?Other Types of RegressionWorking with Lines in the Plane (optional)Scatter Plots with NumPy and Matplotlib (1)Why the Perturbation Technique is UsefulScatter Plots with NumPy and Matplotlib (2)A Quadratic Scatter Plot with NumPy and MatplotlibThe Mean Squared Error (MSE) FormulaA List of Error TypesNon-linear Least SquaresCalculating the MSE ManuallyApproximating Linear Data with np.linspace()Calculating MSE with np.linspace() APISummary
Chapter 3: Classifiers in Machine Learning
What is Classification?What are Classifiers?Common ClassifiersBinary versus Multiclass ClassificationMultilabel ClassificationWhat are Linear Classifiers?What is kNN?How to Handle a Tie in kNNWhat are Decision Trees?What are Random Forests?What are SVMs?Tradeoffs of SVMsWhat is Bayesian Inference?Bayes’ TheoremSome Bayesian TerminologyWhat is MAP?Why Use Bayes’ Theorem?What is a Bayesian Classifier?Types of Naïve Bayes’ ClassifiersTraining ClassifiersEvaluating ClassifiersWhat are Activation Functions?Why Do We Need Activation Functions?How Do Activation Functions Work?Common Activation FunctionsActivation Functions in PythonThe ReLU and ELU Activation FunctionsThe Advantages and Disadvantages of ReLUELUSigmoid, Softmax, and Hardmax SimilaritiesSoftmaxSoftplusTanhSigmoid, Softmax, and HardMax DifferencesWhat is Logistic Regression?Setting a Threshold ValueLogistic Regression: Important AssumptionsLinearly Separable DataSummary

Chapter 4: ChatGPT and GPT-4
What is Generative AI?Important Features of Generative AIPopular Techniques in Generative AIWhat Makes Generative AI UniqueConversational AI versus Generative AIPrimary ObjectivesApplicationsTechnologies UsedTraining and InteractionEvaluationData RequirementsIs DALL-E Part of Generative AI?Are ChatGPT and GPT-4 Part of Generative AI?DeepMindDeepMind and GamesPlayer of Games (PoG)OpenAICohereHugging FaceHugging Face LibrariesHugging Face Model HubAI21InflectionAIAnthropicWhat is Prompt Engineering?Prompts and CompletionsTypes of PromptsInstruction PromptsReverse PromptsSystem Prompts versus Agent PromptsPrompt TemplatesPrompts for Different LLMsPoorly Worded PromptsWhat is ChatGPT?ChatGPTChatGPT: Google “Code Red”ChatGPT versus Google SearchChatGPT Custom InstructionsChatGPT on Mobile Devices and BrowsersChatGPT and PromptsGPTBotChatGPT PlaygroundPlugins, Advanced Data Analysis, and Code WhispererPluginsAdvanced Data AnalysisAdvanced Data Analysis Versus Claude 2Code WhispererDetecting Generated TextConcerns about ChatGPTCode Generation and Dangerous TopicsChatGPT Strengths and WeaknessesSample Queries and Responses from ChatGPTAlternatives to ChatGPTGoogle GeminiYouChatPi from InflectionMachine Learning and ChatGPT: Advanced Data AnalysisWhat is InstructGPT?VizGPT and Data VisualizationWhat is GPT-4?GPT-4 and Test-Taking ScoresGPT-4 ParametersGPT-4 Fine TuningChatGPT and GPT-4 CompetitorsGeminiCoPilot (OpenAI/Microsoft)Codex (OpenAI)Apple GPTPaLM-2Med-PaLM MClaude 2Llama 2How to Download Llama 2Llama 2 Architecture FeaturesFine Tuning Llama 2When Will GPT-5 Be Available?Summary
Chapter 5: Linear Regression with GPT-4
What is Linear Regression?Examples of Linear RegressionMetrics for Linear RegressionCoefficient of Determination (R^2)Linear Regression with Random Data with GPT-4Linear Regression with a Dataset with GPT-4Descriptions of the Features of the death.csv DatasetThe Preparation Process of the DatasetThe Exploratory AnalysisDetailed EDA on the death.csv DatasetBivariate and Multivariate AnalysesThe Model Selection ProcessCode for Linear Regression with the death.csv DatasetDescribe the Model DiagnosticsDescribe Additional Model DiagnosticsMore Recommendations from GPT-4Summary
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 AlgorithmCross-ValidationBias-Variance TradeoffDistance MetricSquare Root RuleDomain KnowledgeEven versus Odd kComputational EfficiencyDiversity in the DatasetThe Elbow Method for the kNN AlgorithmA Machine Learning Model with the kNN AlgorithmA Machine Learning Model with the Decision Tree AlgorithmA Machine Learning Model with the Random Forest AlgorithmA Machine Learning Model with the SVM AlgorithmThe Logistic Regression AlgorithmThe Naïve Bayes AlgorithmThe SVM AlgorithmThe Decision Tree AlgorithmThe Random Forest AlgorithmSummary
Chapter 7: Machine Learning Clustering with GPT-4
What is Clustering?Ten Clustering AlgorithmsMetrics for Clustering AlgorithmsK-means ClusteringHierarchical ClusteringDBSCAN (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 AlgorithmA Machine Learning Model with the Hierarchical Clustering AlgorithmA Machine Learning Model with the DBSCAN AlgorithmSummary
Chapter 8: ChatGPT and Data Visualization
Working with Charts and GraphsBar ChartsPie ChartsLine GraphsHeat MapsHistogramsBox PlotsPareto ChartsRadar ChartsTreemapsWaterfall ChartsLine Plots with MatplotlibPie Charts Using MatplotlibBox and Whisker Plots Using MatplotlibTime Series Visualization with MatplotlibStacked Bar Charts with MatplotlibDonut Charts Using Matplotlib3D Surface Plots with MatplotlibRadial (or Spider) Charts with MatplotlibMatplotlib’s Contour PlotsStreamplots for Vector FieldsQuiver Plots for Vector FieldsPolar PlotsBar Charts with SeabornScatter Plots with Regression Lines Using SeabornHeatmaps for Correlation Matrices with SeabornHistograms with SeabornViolin Plots with SeabornPair Plots Using SeabornFacet Grids with SeabornHierarchical ClusteringSwarm PlotsJoint Plots for Bivariate DataPoint Plots for Factorized ViewsSeaborn’s KDE Plots for Density EstimationsSeaborn’s Ridge PlotsSummary
Index

Content preview from Python 3 and Machine Learning Using ChatGPT/GPT-4

CHAPTER 2

INTRODUCTION TO MACHINE LEARNING

This chapter introduces numerous concepts in machine learning, such as feature selection, feature engineering, data cleaning, training sets, and test sets.

The first part of this chapter briefly discusses machine learning and the sequence of steps that are typically required to prepare a dataset. These steps include “feature selection” or “feature extraction” that can be performed using various algorithms.

The second section describes the types of data that you can encounter, issues that can arise with the data in datasets, and how to rectify them. You will also learn about the difference between “hold out” and “k-fold” when you perform the training step.

The third part of this chapter briefly discusses ...

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Python 3 and Machine Learning Using ChatGPT/GPT-4

by Oswald Campesato

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