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Debugging Machine Learning Models with Python · LearnSpace
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Debugging Machine Learning Models with Python

Курс от Packt
Средний≈ 10.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Debugging machine learning systems is a critical skill for building reliable, trustworthy, and high-performing AI solutions. This course teaches you how to identify, diagnose, and resolve issues throughout the machine learning lifecycle, helping you create models that are accurate, efficient, explainable, and production-ready. You will learn practical techniques to evaluate model behavior, improve performance, detect bias, manage risks, and implement testing strategies for machine learning applications. Through hands-on exploration of Python-based workflows, you will develop the ability to build reproducible pipelines, address data and concept drift, and strengthen model reliability in real-world environments. Unlike courses that focus only on model development, this course emphasizes systematic debugging and responsible AI practices. It combines foundational machine learning concepts with advanced topics such as deep learning, explainability, causality, security, privacy, and human-in-the-loop machine learning to bridge the gap between theory and industrial deployment. This course is ideal for data scientists, machine learning engineers, analysts, AI practitioners, and Python developers seeking to improve model quality and operational excellence. Learners should have basic Python programming knowledge and familiarity with machine learning concepts; the course is designed at an intermediate level.

Навыки, которые вы освоите

Machine LearningModel OptimizationModel EvaluationTestabilityDeep LearningModel TrainingApplied Machine LearningDebuggingPyTorch (Machine Learning Library)Test Driven Development (TDD)AI SecurityPython ProgrammingMLOps (Machine Learning Operations)Verification And ValidationMachine Learning MethodsResponsible AIModel DeploymentAI EnablementTest ToolsData Preprocessing

Программа курса

17 модулей · 102 учебных материалов

01Beyond Code Debugging9 материалов

Mastering Debugging: From Code Errors to Model Insights

OverviewВидеоIntroductionЧтениеTypes of Machine Learning ModelingЧтениеDebugging in Software DevelopmentЧтение

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Packt - Course Instructors

Преподаватель курса

Debugging Machine Learning Models with Python
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 10.7 ч

17 модулей

Язык: Английский

Часть программы вашего университета
TracebackЧтение
Incremental ProgrammingЧтение
Flaws in Data Used for ModelingЧтение
Model and Prediction-Centric DebuggingЧтение
Foundations of Machine Learning and DebuggingЗадание
02Machine Learning Life Cycle9 материалов

From Raw Data to Deployed Models: Navigating the Machine Learning Workflow

OverviewВидеоIntroductionЧтениеData CollectionЧтениеData WranglingЧтениеFeature Imputation for Filling in Missing ValuesЧтениеData ScalingЧтениеDesigning an Evaluation and Testing StrategyЧтениеTesting the Code and the ModelЧтениеMachine Learning Life Cycle FundamentalsЗадание
03Debugging toward Responsible AI7 материалов

Building Trustworthy AI: Addressing Bias, Security, and Transparency

OverviewВидеоIntroductionЧтениеMeasurement or Labeling BiasЧтениеOutput Integrity AttacksЧтениеTransparency in Machine Learning ModelingЧтениеAccountable and Open to Inspection ModelingЧтениеEthical Considerations in AI DevelopmentЗадание
04Detecting Performance and Efficiency Issues in Machine Learning Models8 материалов

Mastering Model Evaluation: Metrics, Validation, and Error Diagnosis

OverviewВидеоIntroductionЧтениеProbability-based Performance MetricsЧтениеClusteringЧтениеBias and Variance DiagnosisЧтениеModel Validation StrategyЧтениеError AnalysisЧтениеEvaluating Machine Learning Model PerformanceЗадание
05Improving the Performance of Machine Learning Models8 материалов

Mastering Model Optimization: From Data Augmentation to Regularization

OverviewВидеоIntroductionЧтениеGrid SearchЧтениеSynthetic Data GenerationЧтениеImproving Pre-training Data ProcessingЧтениеBenefitting from Data of Lower Quality or RelevanceЧтениеRegularization to Improve Model GeneralizabilityЧтениеEnhancing Machine Learning Model EffectivenessЗадание
06Interpretability and Explainability in Machine Learning Modeling6 материалов

Demystifying Machine Learning: Hands-On Explainability with SHAP and Counterfactuals

OverviewВидеоIntroductionЧтениеLocal Explanation Using SHAPЧтениеSummaries of CounterfactualsЧтениеGlobal ExplanationЧтениеInterpreting Machine Learning ModelsЗадание
07Decreasing Bias and Achieving Fairness6 материалов

Uncovering and Addressing Bias in Machine Learning Systems

OverviewВидеоIntroductionЧтениеProxies for Sensitive VariablesЧтениеBias in ProductionЧтениеFairness Assessment and Improvement in PythonЧтениеFairness and Bias in Machine Learning ModelsЗадание
08Controlling Risks Using Test-Driven Development5 материалов

Building Reliable Machine Learning Workflows with Testing and Experiment Tracking

OverviewВидеоIntroductionЧтениеPytest FixturesЧтениеTracking Machine Learning ExperimentsЧтениеTesting and Risk Management in Software DevelopmentЗадание
09Testing and Debugging for Production5 материалов

Ensuring Reliable ML Deployments: Testing, Integration, and Live Monitoring

OverviewВидеоIntroductionЧтениеIntegration Testing of Machine Learning PipelinesЧтениеMonitoring and Validating Live PerformanceЧтениеTesting and Debugging in Production SystemsЗадание
10Versioning and Reproducible Machine Learning Modeling4 материалов

Mastering Reproducibility: Data and Model Versioning in ML Workflows

OverviewВидеоIntroductionЧтениеData VersioningЧтениеEnsuring Reliable Machine Learning WorkflowsЗадание
11Avoiding and Detecting Data and Concept Drifts4 материалов

Mastering Drift Detection for Reliable Machine Learning

OverviewВидеоIntroductionЧтениеDetecting DriftsЧтениеMonitoring and Managing Drift in Machine Learning ModelsЗадание
12Going Beyond ML Debugging with Deep Learning5 материалов

Mastering Neural Networks: From Fundamentals to Fine-Tuning

OverviewВидеоIntroductionЧтениеOptimization AlgorithmsЧтениеHyperparameter Tuning for Deep LearningЧтениеExploring Advanced Deep Learning ConceptsЗадание
13Advanced Deep Learning Techniques8 материалов

Mastering Deep Learning Across Images, Text, and Graphs

OverviewВидеоIntroductionЧтениеConvolutional Neural Networks for Image Shape DataЧтениеImage Data Transformation and Augmentation for CNNsЧтениеTokenizationЧтениеLanguage Modeling Using Pre-Trained ModelsЧтениеGraph Neural NetworksЧтениеExploring Deep Learning InnovationsЗадание
14Introduction to Recent Advancements in Machine Learning6 материалов

Unveiling the Frontiers: Generative Models, RL, and Self-Supervised Learning in Practice

OverviewВидеоIntroductionЧтениеPrompt Engineering for Text-Based Generative ModelsЧтениеReinforcement LearningЧтениеSelf-Supervised Learning (SSL)ЧтениеExploring Modern Machine Learning InnovationsЗадание
15Correlation versus Causality5 материалов

Unraveling Causal Relationships in Machine Learning

OverviewВидеоIntroductionЧтениеAssessing Causation in Machine Learning ModelsЧтениеCausal Modeling Using PythonЧтениеCorrelation and Causality in Data AnalysisЗадание
16Security and Privacy in Machine Learning4 материалов

Safeguarding Data: Techniques for Secure and Private Machine Learning

OverviewВидеоIntroductionЧтениеHomomorphic EncryptionЧтениеSecurity and Privacy in Machine Learning ConceptsЗадание
17Human-in-the-Loop Machine Learning3 материалов

Harnessing Human Insight for Smarter Machine Learning

OverviewВидеоHuman-in-the-Loop Machine Learning - The ReadingЧтениеHuman-in-the-Loop Machine Learning FundamentalsЗадание