book

Machine Learning Fundamentals

Name: Machine Learning Fundamentals
Author: Hyatt Saleh
ISBN: 9781789803556

by Hyatt Saleh

November 2018

Intermediate to advanced

240 pages

4h 37m

English

Packt Publishing

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Preface
About the BookAbout the AuthorObjectivesAudienceApproachMinimum Hardware RequirementsSoftware RequirementsInstallation and SetupInstalling the Code BundleAdditional ResourcesConventions
Introduction to Scikit-Learn
IntroductionScikit-LearnAdvantages of Scikit-LearnDisadvantages of Scikit-LearnData RepresentationTables of DataFeatures and Target MatricesExercise 1: Loading a Sample Dataset and Creating the Features and Target MatricesActivity 1: Selecting a Target Feature and Creating a Target MatrixData PreprocessingMessy DataExercise 2: Dealing with Messy DataDealing with Categorical FeaturesExercise 3: Applying Feature Engineering over Text DataRescaling DataExercise 4: Normalizing and Standardizing DataActivity 2: Preprocessing an Entire DatasetScikit-Learn APIHow Does It Work?Supervised and Unsupervised LearningSupervised LearningUnsupervised LearningSummary
Unsupervised Learning: Real-Life Applications
IntroductionClusteringClustering TypesApplications of ClusteringExploring a Dataset: Wholesale Customers DatasetUnderstanding the DatasetData VisualizationLoading the Dataset Using PandasVisualization ToolsExercise 5: Plotting a Histogram of One Feature from the Noisy Circles DatasetActivity 3: Using Data Visualization to Aid the Preprocessing Processk-means AlgorithmUnderstanding the AlgorithmExercise 6: Importing and Training the k-means Algorithm over a DatasetActivity 4: Applying the k-means Algorithm to a DatasetMean-Shift AlgorithmUnderstanding the AlgorithmExercise 7: Importing and Training the Mean-Shift Algorithm over a DatasetActivity 5: Applying the Mean-Shift Algorithm to a DatasetDBSCAN AlgorithmUnderstanding the AlgorithmExercise 8: Importing and Training the DBSCAN Algorithm over a DatasetActivity 6: Applying the DBSCAN Algorithm to the DatasetEvaluating the Performance of ClustersAvailable Metrics in Scikit-LearnExercise 9: Evaluating the Silhouette Coefficient Score and Calinski–Harabasz Index Activity 7: Measuring and Comparing the Performance of the AlgorithmsSummary
Supervised Learning: Key Steps
IntroductionModel Validation and TestingData PartitionSplit RatioExercise 10: Performing Data Partition over a Sample DatasetCross ValidationExercise 11: Using Cross-Validation to Partition the Train Set into a Training and a Validation SetActivity 8: Data Partition over a Handwritten Digit DatasetEvaluation MetricsEvaluation Metrics for Classification TasksExercise 12: Calculating Different Evaluation Metrics over a Classification TaskChoosing an Evaluation MetricEvaluation Metrics for Regression TasksExercise 13: Calculating Evaluation Metrics over a Regression TaskActivity 9: Evaluating the Performance of the Model Trained over a Handwritten DatasetError AnalysisBias, Variance, and Data MismatchExercise 14: Calculating the Error Rate over Different Sets of Data Activity 10: Performing Error Analysis over a Model Trained to Recognize Handwritten DigitsSummary
Supervised Learning Algorithms: Predict Annual Income
IntroductionExploring the DatasetUnderstanding the DatasetNaïve Bayes AlgorithmHow Does It Work?Exercise 15: Applying the Naïve Bayes Algorithm Activity 11: Training a Naïve Bayes Model for Our Census Income DatasetDecision Tree AlgorithmHow Does It Work?Exercise 16: Applying the Decision Tree Algorithm Activity 12: Training a Decision Tree Model for Our Census Income DatasetSupport Vector Machine AlgorithmHow Does It Work?Exercise 17: Applying the SVM Algorithm Activity 13: Training an SVM Model for Our Census Income DatasetError AnalysisAccuracy, Precision, and RecallSummary
Artificial Neural Networks: Predict Annual Income
IntroductionArtificial Neural NetworksHow Do They Work?Understanding the HyperparametersApplicationsLimitationsApplying an Artificial Neural NetworkScikit-Learn's Multilayer PerceptronExercise 18: Applying the Multilayer Perceptron Classifier Class Activity 14: Training a Multilayer Perceptron for Our Census Income DatasetPerformance AnalysisError AnalysisHyperparameter Fine-TuningModel ComparisonActivity 15: Comparing Different Models to Choose the Best Fit for the Census Income Data ProblemSummary
Building Your Own Program
IntroductionProgram DefinitionBuilding a Program: Key StagesUnderstanding the DatasetActivity 16: Performing the Preparation and Creation Stages for the Bank Marketing DatasetSaving and Loading a Trained ModelSaving a ModelExercise 19: Saving a Trained Model Loading a ModelExercise 20: Loading a Saved Model Activity 17: Saving and Loading the Final Model for the Bank Marketing DatasetInteracting with a Trained ModelExercise 21: Creating a Class and a Channel to Interact with a Trained Model Activity 18: Allowing Interaction with the Bank Marketing Dataset ModelSummary
Appendix
Chapter 1: Introduction to scikit-learnActivity 1: Selecting a Target Feature and Creating a Target MatrixActivity 2: Preprocessing an Entire DatasetChapter 2: Unsupervised Learning: Real-life ApplicationsActivity 3: Using Data Visualization to Aid the Preprocessing ProcessActivity 4: Applying the k-means Algorithm to a DatasetActivity 5: Applying the Mean-Shift Algorithm to a DatasetActivity 6: Applying the DBSCAN Algorithm to the DatasetActivity 7: Measuring and Comparing the Performance of the AlgorithmsChapter 3: Supervised Learning: Key StepsActivity 8: Data Partition over a Handwritten Digit DatasetActivity 9: Evaluating the Performance of the Model Trained over a Handwritten DatasetActivity 10: Performing Error Analysis over a Model Trained to Recognize Handwritten DigitsChapter 4: Supervised Learning Algorithms: Predict Annual IncomeActivity 11: Training a Naïve Bayes Model for our Census Income DatasetActivity 12: Training a Decision Tree Model for our Census Income DatasetActivity 13: Training a SVM Model for our Census Income DatasetChapter 5: Artificial Neural Networks: Predict Annual IncomeActivity 14: Training a Multilayer Perceptron for our Census Income DatasetActivity 15: Comparing Different Models to Choose the Best Fit for the Census Income Data ProblemChapter 6: Building Your Own ProgramActivity 16: Performing the Preparation and Creation Stages for the Bank Marketing DatasetActivity 17: Saving and Loading the Final Model for the Bank Marketing DatasetActivity 18: Allowing Interaction with the Bank Marketing Dataset Model

Overview

In "Machine Learning Fundamentals," you will learn to harness the power of Python and the scikit-learn library to develop and implement machine learning algorithms effectively. This book explains key concepts in both supervised and unsupervised learning, guiding you through practical real-world applications and scenarios.

What this Book will help me do

Understand the difference between supervised and unsupervised learning models and their appropriate use cases.
Apply algorithms such as k-means clustering and decision trees to solve real-world machine learning problems.
Understand the process of data representation and its importance in building effective machine learning models.
Use Python's scikit-learn package to implement machine learning techniques and analyze results.
Gain experience with hyperparameter tuning to optimize model performance.

Author(s)

Hyatt Saleh is a seasoned software engineer and data scientist with years of experience in machine learning and Python programming. Having worked on various machine learning projects, Hyatt brings a practical perspective to this book, blending academic concepts with real-world solutions to common challenges.

Who is it for?

This book is ideal for developers who are newcomers to machine learning and wish to understand its core principles and applications. If you have experience in Python programming but are new to scikit-learn or machine learning frameworks, this book will provide you with a comprehensive start. Through practical hands-on examples and clear explanations, it equips you with the skills to build machine learning applications for real-world usage.

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