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Machine Learning: Concepts and Applications · LearnSpace
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Machine Learning: Concepts and Applications

Курс от The University of Chicago
Средний≈ 37.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning. A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy. It is based on an introductory machine learning course offered to graduate students at the University of Chicago and will serve as a strong foundation for deeper and more specialized study.

Навыки, которые вы освоите

Model EvaluationRandom Forest AlgorithmDecision Tree LearningData PreprocessingModel TrainingScikit Learn (Machine Learning Library)Supervised LearningDeep LearningMachine Learning AlgorithmsStatistical Machine LearningRegression AnalysisArtificial Intelligence and Machine Learning (AI/ML)Unsupervised LearningMachine LearningDimensionality ReductionClassification AlgorithmsMachine Learning SoftwareKeras (Neural Network Library)Machine Learning MethodsApplied Machine Learning

Программа курса

9 модулей · 81 учебных материалов

01Machine Learning and the Machine Learning Pipeline11 материалов

Course Introduction + Data Manipulation

Course IntroductionВидеоThe Data Science PipelineВидеоData Ingestion and ExplorationВидеоData Basics: Numpy and PandasЛабораторная

Учитесь у экспертов

Dr. Nick Feamster

Professor

Machine Learning: Concepts and Applications
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 37.7 ч

9 модулей

Язык: Английский

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

Часть программы вашего университета
Lab Walkthrough: Data Exploration with PandasВидео
Data Exploration with PandasЛабораторная
Working with DataЗадание

Supervised Learning, Linear Models, and Least Squares

Supervised Learning, Linear Models, and Least SquaresВидеоLab Walkthrough: Linear RegressionВидеоLinear RegressionЛабораторнаяIntroduction to Linear RegressionЗадание
02Least Squares and Maximum Likelihood Estimation9 материалов

Least Squares Estimation and Significance

Linear Regression and Least SquaresВидеоLab Walkthrough: Linear Regression on the Prostate Cancer DatasetВидеоLinear Regression on the Prostate Cancer DatasetЛабораторнаяLinear RegressionЗадание

Maximum Likelihood Estimation

Maximum Likelihood EstimationВидеоLab Walkthrough: Linear Regression and Maximum Likelihood EstimationВидеоLinear Regression and Maximum Likelihood EstimationЛабораторнаяMaximum Likelihood EstimationЗадание

Graded Quiz

Graded Quiz: Manipulating Data & Linear RegressionsПрограммирование
03Basis Functions and Regularization8 материалов

Basis Functions

Basis FunctionsВидеоLab Walkthrough: Features and Basis FunctionsВидеоFeatures and Basis FunctionsЛабораторнаяPolynomial Feature ExpansionЗадание

Regularization and Shrinkage

Regularization and the Bias-Variance TradeoffВидеоLab Walkthrough: Linear Regression: RegularizationВидеоLinear Regression: RegularizationЛабораторнаяRegularizationЗадание
04Model Selection and Logistic Regression8 материалов

Model Selection and Cross Validation

Model Selection and Cross ValidationВидеоLab Walkthrough: Model Selection and PipelinesВидеоModel Selection and PipelinesЛабораторнаяModel Tuning and SelectionЗадание

Logistic Regression

Logistic RegressionВидеоLab Walkthrough: Logistic RegressionВидеоLogistic RegressionЛабораторнаяLogistic RegressionЗадание
05More Classifiers: SVMs and Naive Bayes10 материалов

Support Vector Machines

Support Vector MachinesВидеоLab Walkthrough: Support Vector MachinesВидеоSVMsЛабораторнаяClassification with SVMsЗадание

Naive Bayes Classification

Naive Bayes ClassificationВидеоNaive Bayes Classification ExampleВидеоNaive Bayes Classification ExampleЛабораторнаяNaive Bayes ClassifiersЗадание

Graded Quiz 2

Graded Quiz: Model EvaluationЗаданиеStarter Code for the QuizЛабораторная
06Tree-Based Models, Ensemble Methods, and Evaluation11 материалов

Decision Trees and Ensembles

Tree-Based ModelsВидеоEnsembles, Bagging, and BoostingВидеоLab Walkthrough: Trees and ForestsВидеоTrees and ForestsЛабораторнаяTrees and EnsemblesЗадание

Evaluation Metrics

Evaluation MetricsВидеоLab Walkthrough: EvaluationВидеоEvaluationЛабораторнаяEvaluating ModelsЗадание

Graded Quiz 3

Trees and Forests QuizЗаданиеStarter Code for the QuizЛабораторная
07Clustering Methods8 материалов

K-Means and Hierarchical Clustering

Unsupervised Learning (K-Means, Hierarchical)ВидеоLab Walkthrough: ClusteringВидеоClusteringЛабораторнаяK-Means and Hierarchical ClusteringЗадание

Distribution and Density-Based Clustering

Clustering (KDE, Meanshift, DBSCAN)ВидеоLab Walkthrough: Density and Distribution-Based ClusteringВидеоDensity and Distribution-Based ClusteringЛабораторнаяClustering IIЗадание
08Dimensionality Reduction and Temporal Models8 материалов

Dimensionality Reduction

Principal Component Analysis (PCA)ВидеоLab Walkthrough: Principal Component AnalysisВидеоPrincipal Component Analysis (PCA)ЛабораторнаяPrincipal Component AnalysisЗадание

Hidden Markov Models

Temporal Models and Hidden Markov ModelsВидеоLab Walkthrough: Hidden Markov ModelsВидеоHidden Markov Models on Divvy Bike TripsЛабораторнаяHMMsЗадание
09Deep Learning8 материалов

Feed-Forward Neural Nets

Feed-Forward Neural NetworksВидеоLab Walkthrough: Feed Forward Neural NetworksВидеоFeed-forward Neural NetsЛабораторнаяNeural NetworksЗадание

Convolutional Neural Nets

Convolutional Neural NetworksВидеоLab Walkthrough: Convolutional Neural NetsВидеоConvolutional Neural NetsЛабораторнаяConvolutional Neural NetsЗадание