why-a-case-study-approach-机器学习基础：案例研究（华盛顿大学）-EEWORLD大学堂

课时1：welcome-to-this-course-and-specialization
课时2：who-we-are
课时3：machine-learning-is-changing-the-world
课时4：why-a-case-study-approach
课时5：specialization-overview
课时6：how-we-got-into-ml
课时7：who-is-this-specialization-for
课时8：what-you-ll-be-able-to-do
课时9：the-capstone-and-an-example-intelligent-application
课时10：the-future-of-intelligent-applications
课时11：starting-an-ipython-notebook
课时12：creating-variables-in-python
课时13：conditional-statements-and-loops-in-python
课时14：creating-functions-and-lambdas-in-python
课时15：starting-graphlab-create-loading-an-sframe
课时16：canvas-for-data-visualization
课时17：interacting-with-columns-of-an-sframe
课时18：using-apply-for-data-transformation
课时19：predicting-house-prices-a-case-study-in-regression
课时20：what-is-the-goal-and-how-might-you-naively-address-it
课时21：linear-regression-a-model-based-approach
课时22：adding-higher-order-effects
课时23：evaluating-overfitting-via-training-test-split
课时24：training-test-curves
课时25：adding-other-features
课时26：other-regression-examples
课时27：regression-ml-block-diagram
课时28：loading-exploring-house-sale-data
课时29：splitting-the-data-into-training-and-test-sets
课时30：learning-a-simple-regression-model-to-predict-house-prices-from-house-size
课时31：evaluating-error-rmse-of-the-simple-model
课时32：visualizing-predictions-of-simple-model-with-matplotlib
课时33：inspecting-the-model-coefficients-learned
课时34：exploring-other-features-of-the-data
课时35：learning-a-model-to-predict-house-prices-from-more-features
课时36：applying-learned-models-to-predict-price-of-an-average-house
课时37：applying-learned-models-to-predict-price-of-two-fancy-houses
课时38：analyzing-the-sentiment-of-reviews-a-case-study-in-classification
课时39：what-is-an-intelligent-restaurant-review-system
课时40：examples-of-classification-tasks
课时41：linear-classifiers
课时42：decision-boundaries
课时43：training-and-evaluating-a-classifier
课时44：whats-a-good-accuracy
课时45：false-positives-false-negatives-and-confusion-matrices
课时46：learning-curves
课时47：class-probabilities
课时48：classification-ml-block-diagram
课时49：loading-exploring-product-review-data
课时50：creating-the-word-count-vector
课时51：exploring-the-most-popular-product
课时52：defining-which-reviews-have-positive-or-negative-sentiment
课时53：training-a-sentiment-classifier
课时54：evaluating-a-classifier-the-roc-curve
课时55：applying-model-to-find-most-positive-negative-reviews-for-a-product
课时56：exploring-the-most-positive-negative-aspects-of-a-product
课时57：document-retrieval-a-case-study-in-clustering-and-measuring-similarity
课时58：what-is-the-document-retrieval-task
课时59：word-count-representation-for-measuring-similarity
课时60：prioritizing-important-words-with-tf-idf
课时61：calculating-tf-idf-vectors
课时62：retrieving-similar-documents-using-nearest-neighbor-search
课时63：clustering-documents-task-overview
课时64：clustering-documents-an-unsupervised-learning-task
课时65：k-means-a-clustering-algorithm
课时66：other-examples-of-clustering
课时67：clustering-and-similarity-ml-block-diagram
课时68：loading-exploring-wikipedia-data
课时69：exploring-word-counts
课时70：computing-exploring-tf-idfs
课时71：computing-distances-between-wikipedia-articles
课时72：building-exploring-a-nearest-neighbors-model-for-wikipedia-articles
课时73：examples-of-document-retrieval-in-action
课时74：recommender-systems-overview
课时75：where-we-see-recommender-systems-in-action
课时76：building-a-recommender-system-via-classification
课时77：collaborative-filtering-people-who-bought-this-also-bought
课时78：effect-of-popular-items
课时79：normalizing-co-occurrence-matrices-and-leveraging-purchase-histories
课时80：the-matrix-completion-task
课时81：recommendations-from-known-user-item-features
课时82：predictions-in-matrix-form
课时83：discovering-hidden-structure-by-matrix-factorization
课时84：bringing-it-all-together-featurized-matrix-factorization
课时85：a-performance-metric-for-recommender-systems
课时86：optimal-recommenders
课时87：precision-recall-curves
课时88：recommender-systems-ml-block-diagram
课时89：loading-and-exploring-song-data
课时90：creating-evaluating-a-popularity-based-song-recommender
课时91：creating-evaluating-a-personalized-song-recommender
课时92：searching-for-images-a-case-study-in-deep-learning
课时93：what-is-a-visual-product-recommender
课时94：using-precision-recall-to-compare-recommender-models
课时95：application-of-deep-learning-to-computer-vision
课时96：deep-learning-performance
课时97：demo-of-deep-learning-model-on-imagenet-data
课时98：other-examples-of-deep-learning-in-computer-vision
课时99：challenges-of-deep-learning
课时100：deep-features
课时101：deep-learning-ml-block-diagram
课时102：loading-image-data
课时103：training-evaluating-a-classifier-using-raw-image-pixels
课时104：training-evaluating-a-classifier-using-deep-features
课时105：loading-image-data
课时106：creating-a-nearest-neighbors-model-for-image-retrieval
课时107：querying-the-nearest-neighbors-model-to-retrieve-images
课时108：querying-for-the-most-similar-images-for-car-image
课时109：displaying-other-example-image-retrievals-with-a-python-lambda
课时110：you-ve-made-it
课时111：deploying-an-ml-service
课时112：what-happens-after-deployment
课时113：open-challenges-in-ml
课时114：where-is-ml-going
课时115：whats-ahead-in-the-specialization
课时116：thank-you

课程介绍

在本课程中，您将从一系列实用的案例研究中获得有关机器学习的动手经验。在第一门课程的最后，您将研究如何基于房屋特征预测房价，从用户评论中分析情绪，检索感兴趣的文档，推荐产品以及搜索图像。通过使用这些用例的动手实践，您将能够在广泛的领域中应用机器学习方法。

主讲人简介

Carlos Guestrin
Amazon Professor of Machine Learning

Computer Science and Engineering

Emily Fox
Amazon Professor of Machine Learning
Statistics

大学堂

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