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Supervised Machine Learning: Classification · LearnSpace
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Supervised Machine Learning: Classification

Курс от IBM
Средний≈ 24.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes. By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Навыки, которые вы освоите

Supervised LearningClassification AlgorithmsMachine LearningDecision Tree LearningRandom Forest AlgorithmLogistic RegressionModel EvaluationSampling (Statistics)Applied Machine LearningRegression AnalysisData PreprocessingMachine Learning AlgorithmsModel TrainingData CleansingPredictive ModelingModel OptimizationBusiness LogicMachine Learning MethodsStatistical Machine LearningScikit Learn (Machine Learning Library)

Программа курса

6 модулей · 122 учебных материалов

01Logistic Regression21 материалов

Everything you need to know before starting this course

About this courseЧтениеWelcomeВидеоOptional: Download data assetsЧтение

Logistic Regression: Introduction to Classification and Error Metrics

Introduction: What is Classification?Видео

Учитесь у экспертов

Mark J Grover

Digital Content Delivery Lead

Svitlana (Lana) Kramar

Data Science Content Developer

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

Miguel Maldonado

Machine Learning Curriculum Developer

Supervised Machine Learning: Classification
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 24.3 ч

6 модулей

Язык: Английский

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

Часть программы вашего университета
Introduction to Logistic RegressionВидео
Classification with Logistic RegressionВидео
Logistic Regression with Multi-ClassesВидео
Implementing Logistic Regression ModelsВидео
Confusion Matrix, Accuracy, Specificity, Precision, and RecallВидео
Classification Error Metrics: ROC and Precision-Recall CurvesВидео
Implementing the Calculation of ROC and Precision-Recall CurvesВидео
Logistic Regression Задание

Logistic Regression Labs

Demo Lab: Logistic RegressionВнешний инструмент[Optional] Download Assets for Demo Lab: Logistic Regression Чтение[Optional] Logistic Regression Lab - Part 1Видео[Optional] Logistic Regression Lab - Part 2Видео[Optional] Logistic Regression Lab - Part 3ВидеоPractice Lab: Logistic RegressionВнешний инструментLogistic Regression LabsЗадание

End of module review & evaluation

Summary/ReviewЧтениеModule 1 Graded Quiz Logisitic Regression Задание
02K Nearest Neighbors14 материалов

K Nearest Neighbors

K Nearest Neighbors for ClassificationВидеоK Nearest Neighbors Decision BoundaryВидеоK Nearest Neighbors Distance MeasurementВидеоK Nearest Neighbors Pros and ConsВидеоK Nearest Neighbors with Feature ScalingВидеоK Nearest NeighborsЗадание

K Nearest Neighbors Labs

Demo Lab: K Nearest NeighborsВнешний инструмент[Optional] K Nearest Neighbors Notebook - Part 1Видео[Optional] K Nearest Neighbors Notebook - Part 2Видео[Optional] K Nearest Neighbors Notebook - Part 3ВидеоPractice Lab: K Nearest NeighborsВнешний инструментK Nearest Neighbors LabsЗадание

End of module review & evaluation

Summary/ReviewЧтениеModule 2 Graded Quiz - KNNЗадание
03Support Vector Machines19 материалов

Support Vector Machines

Introduction to Support Vector MachinesВидеоClassification with Support Vector MachinesВидеоThe Support Vector Machines Cost FunctionВидеоRegularization in Support Vector MachinesВидеоSupport Vector MachinesЗадание

Support Vector Machines Kernels

Introduction to Support Vector Machines Gaussian KernelsВидеоSupport Vector Machines Gaussian Kernels - Part 1ВидеоSupport Vector Machines Gaussian Kernels - Part 2ВидеоSupport Vector Machines WorkflowВидеоImplementing Support Vector Machines Kernal ModelsВидеоSupport Vector Machines KernelsЗадание

Support Vector Machines Labs

Demo Lab: Support Vector MachinesВнешний инструмент[Optional] Support Vector Machines Notebook - Part 1Видео[Optional] Support Vector Machines Notebook - Part 2Видео[Optional] Support Vector Machines Notebook - Part 3ВидеоPractice Lab: Support Vector MachinesВнешний инструментSupport Vector Machines LabsЗадание

End of module review

Summary/ReviewЧтениеModule 3 Graded Quiz: Support Vector MachinesЗадание
04Decision Trees16 материалов

Decision Trees

Overview of ClassifiersВидеоIntroduction to Decision TreesВидеоBuilding a Decision TreeВидеоEntropy-based SplittingВидеоOther Decision Tree Splitting CriteriaВидеоPros and Cons of Decision TreesВидеоDecision TreesЗадание

Decision Trees Labs

Demo Lab: Decision TreesВнешний инструмент[Optional] Download Assets for Demo Lab: Decision Trees Чтение[Optional] Decision Trees Notebook - Part 1Видео[Optional] Decision Trees Notebook - Part 2Видео[Optional] Decision Trees Notebook - Part 3ВидеоPractice Lab: Decision TreesВнешний инструмент

End of module review

Summary/ReviewЧтениеModule 4 Graded Quiz: Decision TreesЗадание
05Ensemble Models31 материалов

Ensemble Based Methods and Bagging

Ensemble Based Methods and Bagging - Part 1ВидеоEnsemble Based Methods and Bagging - Part 2ВидеоEnsemble Based Methods and Bagging - Part 3ВидеоBaggingЗадание

Random Forest

Random ForestВидеоPractice Lab: Random ForestВнешний инструментRandom ForestЗадание

Bagging Labs

Demo Lab: BaggingВнешний инструмент[Optional] Download Assets for Demo Lab: Bagging Чтение[Optional] Bagging Notebook - Part 1Видео[Optional] Bagging Notebook - Part 2Видео[Optional] Bagging Notebook - Part 3ВидеоPractice Lab: BaggingВнешний инструмент

Boosting and Stacking

Boosting and StackingВидеоOverview of BoostingВидеоAdaboost and Gradient Boosting OverviewВидеоAdaboost and Gradient Boosting SyntaxВидеоStackingВидеоBoosting and StackingЗадание

Boosting and Stacking Labs

Demo Lab: Boosting and StackingВнешний инструмент[Optional] Download Assets for Demo Lab: Boosting and Stacking Чтение[Optional] Boosting Notebook - Part 1Видео[Optional] Boosting Notebook - Part 2Видео[Optional] Boosting Notebook - Part 3ВидеоPractice Lab: Ada BoostВнешний инструмент

End of module review & evaluation

Summary/ReviewЧтениеModule 5 Graded QuizЗадание
06Modeling Unbalanced Classes21 материалов

Model Interpretability

Model InterpretabilityВидеоExamples of Self-Interpretable and Non-Self-Interpretable ModelsВидеоModel-Agnostic ExplanationsВидеоSurrogate ModelsВидеоPractice Lab: Model InterpretabilityВнешний инструментPractice: Model interpretability Задание

Modeling Unbalanced Classes

Introduction to Unbalanced ClassesВидеоUpsampling and DownsamplingВидеоModeling Approaches: Weighting and Stratified SamplingВидеоModeling Approaches: Random and Synthetic OversamplingВидеоModeling Approaches: Nearing Neighbor MethodsВидеоModeling Approaches: BlaggingВидеоPractice Lab: Modeling Imbalanced ClassesВнешний инструментModeling Unbalanced ClassesЗадание

End of Module Review

Summary/ReviewЧтениеFinal Project OverviewЧтениеReading: Final Submission Guidelines and DeliverablesЧтениеOption 1: AI Graded - Final Project: Submission and EvaluationВнешний инструментOption 2: Peer Graded - Final Project Submission and EvaluationВзаимная проверкаModule 6 Graded Quiz Задание

Course Wrap-Up

Thanks from the Course TeamЧтение
Decision Trees LabsЗадание
Bagging LabsЗадание
Practice Lab: Stacking For Classification with PythonВнешний инструмент
Practice Lab: (Optional) Gradient BoostingВнешний инструмент
Boosting and Stacking LabsЗадание