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Building a Machine Learning Solution · LearnSpace
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Building a Machine Learning Solution

Курс от Coursera
Средний≈ 20.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Welcome to Building a Machine Learning Solution, where you'll journey through the complete lifecycle of a machine learning project. This capstone course covers critical steps from problem definition to deployment and maintenance. You'll learn to define clear problem statements, collect and preprocess data, perform exploratory data analysis (EDA), and engineer features to enhance model performance. The course guides you in selecting and implementing appropriate models, comparing classical machine learning, deep learning, and generative AI approaches. Emphasizing real-world considerations, you'll address scalability, interpretability, and ethical implications. You'll gain hands-on experience with tools like scikit-learn, TensorFlow, PyTorch, and more, ensuring you can deploy and monitor models effectively. By the end of this course, you'll be equipped to build end-to-end ML solutions that transform data into actionable insights, making informed decisions at each stage of development.

Навыки, которые вы освоите

Feature EngineeringMachine LearningResponsible AIData CollectionExploratory Data AnalysisModel EvaluationModel DeploymentData PreprocessingGenerative AIMachine Learning SoftwareData EthicsMachine Learning MethodsStatistical MethodsApplication DeploymentApplied Machine LearningData CleansingModel TrainingContinuous MonitoringStatistical AnalysisData Analysis

Программа курса

5 модулей · 80 учебных материалов

01Problem Definition & Data Collection15 материалов

Defining the ML Problem

What Makes a Real-World ML Project Successful?ВидеоWhat Makes a Problem Statement Good or Bad?ЧтениеFixing and Framing ML Problems Across DomainsЧтениеHow to Identify and Structure an ML ProblemЧтение

Учитесь у экспертов

Professionals from the Industry

Преподаватель курса

Building a Machine Learning Solution
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 20.5 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Success Metrics and Real-World ConstraintsЧтение
Choosing Between Accuracy and FairnessDIALOGUE
Define Your Own ML ProblemЛабораторная
Knowledge Check: ML Problem FormulationЗадание

Data Collection & Preprocessing

Preprocessing Real-World Data for Machine LearningВидеоWhere and How to Source Data for ML ProjectsЧтениеPreprocessing Techniques: Clean, Transform, and Prepare DataЧтениеPrepare Your Dataset for ModelingЛабораторнаяIs It Better to Engineer More Features or Use Raw Data?DIALOGUEKnowledge Check: Data Preprocessing & Feature EngineeringЗаданиеProblem Definition & Data CollectionЗадание
02Exploratory Data Analysis (EDA) & Feature Engineering12 материалов

Exploratory Data Analysis (EDA)

Visualizing and Diagnosing Data with EDAВидеоExploring Distributions and Relationships with Visual EDAЧтениеFinding Correlations and Outliers in Your DataЧтениеPerform EDA on Your Project DatasetЛабораторнаяWhat Should You Do with Outliers?DIALOGUEKnowledge Check: EDA TechniquesЗадание

Feature Engineering & Selection

Transform, Reduce, Select: Advanced Feature EngineeringВидеоFeature Transformation, Extraction, and Selection MethodsЧтениеEngineer and Select Features from Your DatasetЛабораторнаяToo Many Features: Should You Reduce or Select?DIALOGUEKnowledge Check: Feature Engineering & SelectionЗаданиеExploratory Data Analysis & Feature EngineeringЗадание
03Model Selection & Implementation22 материалов

Baseline Models & Performance Benchmarking

Choosing the Right Model Isn't Just About AccuracyВидеоWhy Baselines Matter: Measuring Progress with Simple ModelsЧтениеEstablishing a Baseline – Part 1: Training Simple ModelsВидеоEstablishing a Baseline – Part 2: Evaluation and Model SelectionВидеоTrain and Evaluate Your Baseline ModelsЛабораторнаяShould You Always Move Beyond the Baseline?DIALOGUEKnowledge Check: Baseline Models & MetricsЗадание

Advanced ML Models

Boosting Performance with XGBoost and LightGBMВидеоDeep Learning for Vision and Text: CNNs and Transformers in ActionВидеоGenerative AI in Action: From Noise to Images with Diffusion ModelsВидеоChoosing the Right Advanced Model for the Right TaskЧтениеTrain an Advanced Model on Your DatasetЛабораторнаяWhen to Leave Simplicity BehindDIALOGUE

Ensemble Techniques & Stacking

Bagging vs. Boosting: Comparing Random Forest and XGBoostВидеоStacking for Smart Predictions: Combining Models for Better ResultsВидеоEnsemble Learning Basics: Bagging, Boosting, and StackingЧтениеWhen and How to Use Ensemble Learning in PracticeЧтениеApply Ensemble Learning to Your ProjectЛабораторнаяToo Many Models? Is Stacking Always Worth It?DIALOGUE
04Model Evaluation & Interpretability16 материалов

Evaluation Metrics

Classification & Regression Metrics in ActionВидеоEvaluating Generative Models: From Text to ImagesВидеоCore Evaluation Metrics by ML Task TypeЧтениеEvaluation Metrics for Classification and Regression TasksЧтениеEvaluating Regression and Generative ModelsЧтениеEvaluate Your Model with Appropriate MetricsЛабораторнаяOne Model, Many Metrics — Which Do You Trust?DIALOGUEKnowledge Check: Evaluation MetricsЗадание

Model Interpretability & Bias Mitigation

Understanding Model Interpretability: SHAP, LIME, and AttentionЧтениеFairness in Machine Learning: Detection and MitigationЧтениеExplaining Predictions: Feature Importance with SHAP and PermutationВидеоExplaining Individual Predictions: LIME and Attention in TransformersВидеоInterpret and Audit Your ModelЛабораторнаяIs a Model Fair If It's Accurate?DIALOGUE
05Deployment & Monitoring15 материалов

Deployment Strategy

Why Model Deployment and Monitoring Matter More Than You ThinkВидеоBatch vs. Real-Time Inference: ML in ActionВидеоFrom Notebook to App: APIs, Versioning, and Deployment ToolsВидеоML Deployment Strategies: Batch, Real-Time, and BeyondЧтениеDesign a Deployment Plan for Your ML ModelЧтениеShould You Always Deploy the Most Accurate Model?DIALOGUEKnowledge Check: Deployment ConceptsЗадание

Model Monitoring & Retraining

Detecting Drift and Planning Retraining: Keeping Your Model RelevantВидеоMonitoring and Maintaining Models in ProductionЧтениеDesign a Monitoring & Retraining StrategyЧтениеIf It Works Today, Will It Work Tomorrow?DIALOGUEKnowledge Check: Monitoring & RetrainingЗадание Deployment & MonitoringЗадание
Knowledge Check: Advanced Modeling TechniquesЗадание
Knowledge Check: Ensemble LearningЗадание
Model Selection & ImplementationЗадание
Knowledge Check: Interpretability & FairnessЗадание
Graded Quiz: Model Evaluation & InterpretabilityЗадание
Congratulations on Completing Your Machine Learning Professional Certificate!Видео
Reflecting on Your Machine Learning JourneyDIALOGUE