Machine Learning Fundamentals: Case Studies (University of Washington)

In this course, you'll gain hands-on experience with machine learning from a series of practical case studies. By the end of the first course, you will study how to predict house prices based on house characteristics, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice using these use cases, you will be able to apply machine learning methods in a wide range of domains.

• Chapter 1：welcome-to-this-course-and-specialization (42 seconds)

• Chapter 2：who-we-are (5 minutes and 43 seconds)

• Chapter 3：machine-learning-is-changing-the-world (3 minutes and 8 seconds)

• Chapter 4：why-a-case-study-approach (1 minutes and 16 seconds)

• Chapter 5：specialization-overview (1 minutes and 39 seconds)

• Chapter 6：how-we-got-into-ml (3 minutes and 23 seconds)

• Chapter 7：who-is-this-specialization-for (4 minutes and 41 seconds)

• Chapter 8：what-you-ll-be-able-to-do (57 seconds)

• Chapter 9：the-capstone-and-an-example-intelligent-application (6 minutes and 31 seconds)

• Chapter 10：the-future-of-intelligent-applications (2 minutes and 19 seconds)

• Chapter 11：starting-an-ipython-notebook (5 minutes and 30 seconds)

• Chapter 12：creating-variables-in-python (7 minutes and 15 seconds)

• Chapter 13：conditional-statements-and-loops-in-python (8 minutes and 8 seconds)

• Chapter 14：creating-functions-and-lambdas-in-python (3 minutes and 31 seconds)

• Chapter 15：starting-graphlab-create-loading-an-sframe (4 minutes and 32 seconds)

• Chapter 16：canvas-for-data-visualization (4 minutes and 9 seconds)

• Chapter 17：interacting-with-columns-of-an-sframe (4 minutes and 29 seconds)

• Chapter 18：using-apply-for-data-transformation (5 minutes and 17 seconds)

• Chapter 19：predicting-house-prices-a-case-study-in-regression (1 minutes and 22 seconds)

• Chapter 20：what-is-the-goal-and-how-might-you-naively-address-it (3 minutes and 47 seconds)

• Chapter 21：linear-regression-a-model-based-approach (5 minutes and 34 seconds)

• Chapter 22：adding-higher-order-effects (4 minutes and 11 seconds)

• Chapter 23：evaluating-overfitting-via-training-test-split (6 minutes and 19 seconds)

• Chapter 24：training-test-curves (4 minutes and 22 seconds)

• Chapter 25：adding-other-features (1 minutes and 14 seconds)

• Chapter 26：other-regression-examples (3 minutes and 28 seconds)

• Chapter 27：regression-ml-block-diagram (3 minutes and 59 seconds)

• Chapter 28：loading-exploring-house-sale-data (7 minutes and 11 seconds)

• Chapter 29：splitting-the-data-into-training-and-test-sets (2 minutes and 34 seconds)

• Chapter 30：learning-a-simple-regression-model-to-predict-house-prices-from-house-size (3 minutes and 54 seconds)

• Chapter 31：evaluating-error-rmse-of-the-simple-model (2 minutes and 29 seconds)

• Chapter 32：visualizing-predictions-of-simple-model-with-matplotlib (4 minutes and 52 seconds)

• Chapter 33：inspecting-the-model-coefficients-learned (1 minutes and 18 seconds)

• Chapter 34：exploring-other-features-of-the-data (6 minutes and 24 seconds)

• Chapter 35：learning-a-model-to-predict-house-prices-from-more-features (3 minutes and 23 seconds)

• Chapter 36：applying-learned-models-to-predict-price-of-an-average-house (5 minutes and 7 seconds)

• Chapter 37：applying-learned-models-to-predict-price-of-two-fancy-houses (7 minutes and 20 seconds)

• Chapter 38：analyzing-the-sentiment-of-reviews-a-case-study-in-classification (38 seconds)

• Chapter 39：what-is-an-intelligent-restaurant-review-system (4 minutes and 23 seconds)

• Chapter 40：examples-of-classification-tasks (4 minutes and 51 seconds)

• Chapter 41：linear-classifiers (5 minutes and 6 seconds)

• Chapter 42：decision-boundaries (3 minutes and 57 seconds)

• Chapter 43：training-and-evaluating-a-classifier (4 minutes and 25 seconds)

• Chapter 44：whats-a-good-accuracy (3 minutes and 10 seconds)

• Chapter 45：false-positives-false-negatives-and-confusion-matrices (6 minutes and 28 seconds)

• Chapter 46：learning-curves (5 minutes and 36 seconds)

• Chapter 47：class-probabilities (1 minutes and 53 seconds)

• Chapter 48：classification-ml-block-diagram (3 minutes and 30 seconds)

• Chapter 49：loading-exploring-product-review-data (2 minutes and 53 seconds)

• Chapter 50：creating-the-word-count-vector (2 minutes and 5 seconds)

• Chapter 51：exploring-the-most-popular-product (4 minutes and 37 seconds)

• Chapter 52：defining-which-reviews-have-positive-or-negative-sentiment (4 minutes and 32 seconds)

• Chapter 53：training-a-sentiment-classifier (3 minutes and 17 seconds)

• Chapter 54：evaluating-a-classifier-the-roc-curve (4 minutes and 24 seconds)

• Chapter 55：applying-model-to-find-most-positive-negative-reviews-for-a-product (4 minutes and 43 seconds)

• Chapter 56：exploring-the-most-positive-negative-aspects-of-a-product (4 minutes and 41 seconds)

• Chapter 57：document-retrieval-a-case-study-in-clustering-and-measuring-similarity (35 seconds)

• Chapter 58：what-is-the-document-retrieval-task (1 minutes and 31 seconds)

• Chapter 59：word-count-representation-for-measuring-similarity (6 minutes and 57 seconds)

• Chapter 60：prioritizing-important-words-with-tf-idf (3 minutes and 42 seconds)

• Chapter 61：calculating-tf-idf-vectors (5 minutes and 29 seconds)

• Chapter 62：retrieving-similar-documents-using-nearest-neighbor-search (2 minutes and 23 seconds)

• Chapter 63：clustering-documents-task-overview (2 minutes and 28 seconds)

• Chapter 64：clustering-documents-an-unsupervised-learning-task (4 minutes and 39 seconds)

• Chapter 65：k-means-a-clustering-algorithm (4 minutes and 0 seconds)

• Chapter 66：other-examples-of-clustering (6 minutes and 2 seconds)

• Chapter 67：clustering-and-similarity-ml-block-diagram (7 minutes and 1 seconds)

• Chapter 68：loading-exploring-wikipedia-data (5 minutes and 21 seconds)

• Chapter 69：exploring-word-counts (5 minutes and 53 seconds)

• Chapter 70：computing-exploring-tf-idfs (7 minutes and 9 seconds)

• Chapter 71：computing-distances-between-wikipedia-articles (5 minutes and 38 seconds)

• Chapter 72：building-exploring-a-nearest-neighbors-model-for-wikipedia-articles (3 minutes and 18 seconds)

• Chapter 73：examples-of-document-retrieval-in-action (4 minutes and 16 seconds)

• Chapter 74：recommender-systems-overview (41 seconds)

• Chapter 75：where-we-see-recommender-systems-in-action (19 seconds)

• Chapter 76：building-a-recommender-system-via-classification (4 minutes and 3 seconds)

• Chapter 77：collaborative-filtering-people-who-bought-this-also-bought (6 minutes and 15 seconds)

• Chapter 78：effect-of-popular-items (1 minutes and 5 seconds)

• Chapter 79：normalizing-co-occurrence-matrices-and-leveraging-purchase-histories (6 minutes and 10 seconds)

• Chapter 80：the-matrix-completion-task (5 minutes and 18 seconds)

• Chapter 81：recommendations-from-known-user-item-features (6 minutes and 0 seconds)

• Chapter 82：predictions-in-matrix-form (3 minutes and 45 seconds)

• Chapter 83：discovering-hidden-structure-by-matrix-factorization (7 minutes and 32 seconds)

• Chapter 84：bringing-it-all-together-featurized-matrix-factorization (30 seconds)

• Chapter 85：a-performance-metric-for-recommender-systems (5 minutes and 30 seconds)

• Chapter 86：optimal-recommenders (2 minutes and 10 seconds)

• Chapter 87：precision-recall-curves (7 minutes and 12 seconds)

• Chapter 88：recommender-systems-ml-block-diagram (4 minutes and 53 seconds)

• Chapter 89：loading-and-exploring-song-data (5 minutes and 43 seconds)

• Chapter 90：creating-evaluating-a-popularity-based-song-recommender (5 minutes and 8 seconds)

• Chapter 91：creating-evaluating-a-personalized-song-recommender (5 minutes and 53 seconds)

• Chapter 92：searching-for-images-a-case-study-in-deep-learning (23 seconds)

• Chapter 93：what-is-a-visual-product-recommender (3 minutes and 52 seconds)

• Chapter 94：using-precision-recall-to-compare-recommender-models (4 minutes and 6 seconds)

• Chapter 95：application-of-deep-learning-to-computer-vision (5 minutes and 41 seconds)

• Chapter 96：deep-learning-performance (3 minutes and 5 seconds)

• Chapter 97：demo-of-deep-learning-model-on-imagenet-data (2 minutes and 57 seconds)

• Chapter 98：other-examples-of-deep-learning-in-computer-vision (1 minutes and 30 seconds)

• Chapter 99：challenges-of-deep-learning (2 minutes and 22 seconds)

• Chapter 100：deep-features (6 minutes and 44 seconds)

• Chapter 101：deep-learning-ml-block-diagram (3 minutes and 11 seconds)

• Chapter 102：loading-image-data (3 minutes and 53 seconds)

• Chapter 103：training-evaluating-a-classifier-using-raw-image-pixels (6 minutes and 14 seconds)

• Chapter 104：training-evaluating-a-classifier-using-deep-features (8 minutes and 9 seconds)

• Chapter 105：loading-image-data (2 minutes and 54 seconds)

• Chapter 106：creating-a-nearest-neighbors-model-for-image-retrieval (1 minutes and 56 seconds)

• Chapter 107：querying-the-nearest-neighbors-model-to-retrieve-images (5 minutes and 42 seconds)

• Chapter 108：querying-for-the-most-similar-images-for-car-image (1 minutes and 53 seconds)

• Chapter 109：displaying-other-example-image-retrievals-with-a-python-lambda (4 minutes and 2 seconds)

• Chapter 110：you-ve-made-it (40 seconds)

• Chapter 111：deploying-an-ml-service (3 minutes and 13 seconds)

• Chapter 112：what-happens-after-deployment (7 minutes and 5 seconds)

• Chapter 113：open-challenges-in-ml (8 minutes and 40 seconds)

• Chapter 114：where-is-ml-going (6 minutes and 29 seconds)

• Chapter 115：whats-ahead-in-the-specialization (5 minutes and 38 seconds)

• Chapter 116：thank-you (1 minutes and 32 seconds)