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Applied Unsupervised Learning in Python

Курс от University of Michigan
Продвинутый≈ 31.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

In “Applied Unsupervised Learning in Python,” you will learn how to use algorithms to find interesting structure in datasets. You will practice applying, interpreting, and refining unsupervised machine learning models to solve a diverse set of problems on real-world datasets. This course will show you how to explore unlabelled data using several techniques: dimensionality reduction and manifold learning for condensing and visualizing high-dimensional data, clustering to reveal interesting groups and outliers, topic modeling for summarizing important themes in text, methods for dealing with missing data, and more. This course also covers best practices associated with different techniques, as well as demonstrating how unsupervised learning can be used to improve supervised prediction. This is the second course in “More Applied Data Science with Python,” a four-course series focused on helping you apply advanced data science techniques using Python. It is recommended that all learners complete the Applied Data Science with Python specialization prior to beginning this course.

Навыки, которые вы освоите

Unsupervised LearningEmbeddingsData PreprocessingModel EvaluationData TransformationAnomaly DetectionUnstructured DataMachine Learning MethodsExploratory Data AnalysisApplied Machine LearningData QualityPython Programming

Программа курса

4 модулей · 88 учебных материалов

01Basic Unsupervised Learning Methods35 материалов

Welcome to the Course

Welcome to Applied Unsupervised Learning in PythonВидеоMADSwPy Certificate Roadmap ЧтениеCourse SyllabusЧтениеAdditional ResourcesЧтение

Учитесь у экспертов

Kevyn Collins-Thompson

Associate Professor of Information, Associate Professor of Electrical Engineering & Computer Science

Applied Unsupervised Learning in Python
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 31.2 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Казахский, Венгерский

Часть программы вашего университета
Meet Your Fellow LearnersОбсуждение
Help Us Learn About YouЧтение

Lesson 1: Methods for Dimensionality Reduction

Dimensionality Reduction: A Brief IntroductionВидеоDimensionality Reduction with Feature Selection: Information GainВидеоTime to Practice: Dimensionality ReductionЗаданиеDimensionality Reduction with Feature Selection: Principal Component Analysis (PCA) ExplainedВидеоVisualizing PCA Results: FoundationsВидеоVisualizing PCA Results: Biplots and Variance PlotsВидеоTen Quick Tips for Effective Dimensionality ReductionЧтениеTime to Practice: Principal Component Analysis (PCA)ЗаданиеSingular Value Decomposition (SVD)ВидеоApplications of SVD in Data ScienceВидеоTime to Practice: Singular Value Decomposition (SVD)ЗаданиеManifold Learning: Multidimensional Scaling (Part 1)ВидеоManifold Learning: Multidimensional Scaling (Part 2)ВидеоTime to Practice: Manifold Learning (Multidimensional scaling: Parts 1 & 2)ЗаданиеManifold Learning: t-Distributed Stochastic Neighbor Embedding (t-SNE)ВидеоManifold Learning: Uniform Manifold Approximation and Projection (UMAP)ВидеоTime to Practice: Manifold Learning (t-SNE, and UMAP)Задание

Lesson 2: Density Estimation

Density Estimation Part 1: Probability Density FunctionsВидеоDensity Estimation Part 1: Parametric vs. Non-Parametric Density EstimatorВидеоTime to Practice: Density Estimation Methods (Part 1)ЗаданиеDensity Estimation Part 2: Local Density EstimatorsВидеоDensity Estimation Part 2: Kernel Density EstimatorsВидеоDensity Estimation Part 2: Evaluating Density EstimatorsВидеоDensity Estimation Part 3: Local Density Estimators and Gaussian Mixture Models (GMMs)ВидеоTime to Practice: Density Estimation Methods (Parts 2 & 3)Задание

Module 1 Wrap-Up

Introduction to Module 1 Programming Assignment: An Introduction to Unsupervised LearningЧтениеCreate & Submit Module 1 AssignmentПрограммированиеAssignment 1 Follow-UpDIALOGUEModule 1 Optional Readings & ResourcesЧтение
02Clustering20 материалов

Lesson 1: Hierarchical Clustering Methods

A Brief Introduction to Clustering (Part 1)ВидеоA Brief Introduction to Clustering (Part 2)ВидеоTime to Practice: Clustering OverviewЗаданиеHierarchical Clustering Part 1: IntroductionВидеоHierarchical Clustering Part 2: Ward's MethodВидеоHierarchical Clustering Part 2: DendogramsВидеоTime to Practice: Hierarchical ClusteringЗадание

Lesson 2: Partitioning Clustering Methods

Introduction to K-meansВидеоApplying K-means in PracticeВидеоTime to Practice: K-means ClusteringЗадание

Lesson 3: Density-Based Clustering Methods

DBSCAN ClusteringВидеоTime to Practice: DBSCAN ClusteringЗадание

Lesson 4: Advanced Clustering Methods

Evaluating Cluster Quality (Part 1)ВидеоEvaluating Cluster Quality (Part 2)ВидеоCluster LabelingЧтениеTime to Practice: Cluster QualityЗадание

Module 2 Wrap-Up

Introduction to Module 2 Assignment: ClusteringЧтениеCreate & Submit Module 2 AssignmentПрограммированиеAssignment 2 FollowupDIALOGUEModule 2 Optional Readings & ResourcesЧтение
03Unsupervised Methods for Text Analysis17 материалов

Lesson 1: Text Representations

How to Represent Text as a Vector: A Typical WorkflowВидеоTime to Practice: Representing Text as a VectorЗаданиеText Processing in SciKit-LearnВидеоTime to Practice: Text Processing in SciKit-LearnЗадание

Lesson 2: Topic Modeling Methods & Techniques

Introduction to Topic ModelingВидеоLatent Dirichlet Allocation (LDA) ВидеоUsing LDA with Scikit-LearnВидеоTime to Practice: Latent Dirichlet Allocation (LDA) ЗаданиеNon-Negative Matrix Factorization (NMF)ВидеоTime to Practice: Non-Negative Matrix Factorization (NMF)Задание

Lesson 3: Word Embeddings Techniques

Word Embeddings Technique #1: Word2vecВидеоWord Embeddings Technique #2: GloveВидеоTime to Practice: Word2vec & GloveЗадание

Module 3 Wrap-Up

Introduction to Module 3 Assignment: Text Representations, Topic Modeling, and Word EmbeddingsЧтениеCreate & Submit Module 3 AssignmentПрограммированиеAssignment 3 FollowupDIALOGUEModule 3 Optional Readings & ResourcesЧтение
04Applications and Variants of Unsupervised Learning16 материалов

Lesson 1: Unsupervised and Supervised Learning

Applying Unsupervised Learning to Supervised Learning TasksВидеоTime to Practice: Applying Unsupervised Learning to Supervised Learning TasksЗадание

Lesson 2: Handling Missing Data

Imputation of Missing DataВидеоImputation with Scikit-LearnВидеоTime to Practice: Imputation of Missing DataЗадание

Lesson 3: Semi-Supervised Learning

A Brief Introduction to Semi-Supervised LearningВидеоLabel propagation with scikit-learnВидеоTime to Practice: Semi-Supervised LearningЗадание

Lesson 4: Self-Supervised Learning

A Brief Introduction to Self-Supervised LearningВидеоTime to Practice: Self-Supervised LearningЗадание

Lesson 5: Module 4 Wrap-Up

Introduction to Module 4 Assignment: Applying Methods and Techniques for Data Imputation and Semi-Supervised LearningЧтениеCreate & Submit Module 4 AssignmentПрограммированиеAssignment 4 FollowupDIALOGUEModule 4 Optional Readings & ResourcesЧтениеCourse ConclusionВидеоPost-Course SurveyЧтение