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Machine Learning Capstone · LearnSpace
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Machine Learning Capstone

Курс от IBM
Продвинутый≈ 19.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning Professional Certificate.   In this course, you will also learn to build a course recommender system, analyze course-related datasets, calculate cosine similarity, and create a similarity matrix. Additionally, you will generate recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering.  Finally, you will share your work with peers and have them evaluate it, facilitating a collaborative learning experience. 

Навыки, которые вы освоите

Unsupervised LearningApplied Machine LearningExploratory Data AnalysisSupervised LearningRegression AnalysisMachine LearningData PresentationMachine Learning AlgorithmsPredictive AnalyticsStatistical AnalysisPredictive ModelingTechnical CommunicationDescriptive StatisticsText MiningScikit Learn (Machine Learning Library)Artificial Neural NetworksData AnalysisPython ProgrammingCollaborative SoftwareKeras (Neural Network Library)

Программа курса

5 модулей · 44 учебных материалов

01Machine Learning Capstone Overview12 материалов

Welcome

Introduction to Machine Learning CapstoneВидеоCapstone OverviewPLUGIN

Exploratory Data Analysis and Feature Engineering

Introduction to Recommender SystemsВидеоReading: Text AnalysisPLUGIN

Учитесь у экспертов

Yan Luo

Ph.D., Data Scientist and Developer

Artem Arutyunov

Data Scientist

Machine Learning Capstone
В каталоге вашей программы

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Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 19.7 ч

5 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
Reading: Stopwords and WordCloudPLUGIN
Exploratory Data Analysis on Online Course Enrollment DataВнешний инструмент
Extract Bag of Words (BoW) Features from Course Textual ContentВнешний инструмент
Reading: Sparse and Dense Bag of Words (BOW) VectorsPLUGIN
Reading: Similarity Measures in Recommender SystemsPLUGIN
Calculate Course Similarity using BoW FeaturesВнешний инструмент
Checkpoints: Exploratory Data Analysis on Online Course Enrollment DataЗадание
Graded: Exploratory Data Analysis and Feature EngineeringЗадание
02 Unsupervised-Learning Based Recommender System8 материалов

Introduction to Content-Based Recommender System

Content-based Recommender SystemsВидеоReading: Evaluation Metrics of Recommender SystemsPLUGINContent-based Course Recommender System using User Profile and Course GenresВнешний инструмент

Similarity-Based Recommender Systems

Reading: Heatmaps PLUGINContent-based Course Recommender System using Course SimilaritiesВнешний инструментClustering-based Course Recommender SystemВнешний инструментCheckpoints: Unsupervised-Learning Based Recommender SystemЗаданиеGraded: Unsupervised-Learning Based Recommendation SystemsЗадание
03Supervised-Learning Based Recommender Systems9 материалов

Introduction to Collaborative Filtering Based Recommender Systems

Collaborative Filtering-Based Recommender SystemsВидеоReading: Exploring Surprise Library and KNN ModelPLUGINCollaborative Filtering-based Recommender System using K Nearest NeighborВнешний инструментCollaborative Filtering-based Recommender System using Non-negative Matrix FactorizationВнешний инструмент

Predictive Model Based Recommender Systems

Course Rating Prediction using Neural NetworksВнешний инструментRegression-based Rating Score Prediction Using Embedding FeaturesВнешний инструментClassification-based Rating Mode Prediction using Embedding FeaturesВнешний инструментCheckpoints: Supervised-Learning Based Recommender SystemsЗаданиеGraded: Supervised-Learning Based Recommendation MethodsЗадание
04Share and Present Your Recommender Systems6 материалов

How to Present Your Findings

Elements Of A Successful Data Findings ReportВидеоReading: Structure Of A ReportPLUGINBest Practices For Presenting Your FindingsВидео(Optional) Hands-on Lab: Getting Started With PowerPoint For The WebPLUGIN(Optional) Hands-on Lab: Basics of PowerPointPLUGIN(Optional) Hands-on Lab: Save your PowerPoint Presentation as PDFPLUGIN
05Final Submission9 материалов

Final Submission and Instructions

Final Project OverviewPLUGINFinal Project Submission Guidelines and DeliverablesPLUGINOption 1: AI Graded - Final Project: Submission and EvaluationВнешний инструментOption 2: Peer Graded - Final Project Submission and EvaluationВзаимная проверка

(Optional) Deploy and Showcase Your Models

An Overview of the Streamlit ModuleЧтениеIntroduction to StreamlitPLUGINBuild a Course Recommender App with StreamlitPLUGIN

Course Wrap-Up

Congratulations and Next StepsЧтениеThanks from the Course TeamЧтение