[CourserHub.com] Coursera - Machine Learning Algorithms in the Real World Specialization

Creation Time	April 17, 2025, 1:53 p.m.
Last Access Time	Oct. 18, 2025, 4:37 a.m.
File Size	2.8 GB
Keywords	Real Specialization - Coursera Machine Algorithms Learning in World the com CourserHub
Total Requests	305
Total Files	465
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/03_business-understanding-and-problem-discovery.mp4 49.0 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/04_what-is-artificial-intelligence-and-machine-learning.mp4 43.9 MB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/01_data-acquisition-and-understanding.mp4 42.0 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/01_data-warehousing.mp4 40.3 MB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/04_features-and-transformations-of-raw-data.mp4 39.1 MB
machine-learning-applied/02_machine-learning-in-the-real-world/01_lesson-1-machines-are-different-from-humans/02_features-and-transformations-of-raw-data.mp4 39.1 MB
data-machine-learning/04_bad-data/01_accept-limitations/02_generalization-and-how-machines-actually-learn.mp4 37.5 MB
machine-learning-applied/02_machine-learning-in-the-real-world/01_lesson-1-machines-are-different-from-humans/01_generalization-and-how-machines-actually-learn.mp4 37.5 MB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/02_distance-measures.mp4 36.7 MB
data-machine-learning/03_feature-engineering-for-more-fun-profit/03_transfer-learning/01_transfer-learning.mp4 36.7 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/03_reinforcement-learning.mp4 33.3 MB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/01_classification-using-k-nearest-neighbours.mp4 33.1 MB
data-machine-learning/03_feature-engineering-for-more-fun-profit/01_understanding-features/01_what-are-the-simplest-features-to-try.mp4 33.1 MB
data-machine-learning/04_bad-data/03_consequences-of-bad-data/02_live-data-danger.mp4 33.0 MB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/01_models-with-transfer-functions/02_neural-networks.mp4 32.7 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/06_the-machine-learning-process.mp4 32.2 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/02_coordinate/01_everything-has-to-be-numbers.mp4 31.8 MB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/05_exploring-the-process-of-problem-definition.mp4 31.5 MB
machine-learning-applied/04_machine-learning-projects/02_lesson-2-getting-ready-to-model/01_exploring-the-process-of-problem-definition.mp4 31.5 MB
data-machine-learning/04_bad-data/02_statistical-nuance/02_skewed-distributions.mp4 31.2 MB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/01_mapping-the-model-lifecycle/02_post-deployment-challenges.mp4 29.6 MB
data-machine-learning/03_feature-engineering-for-more-fun-profit/02_building-good-features/02_feature-selection.mp4 29.6 MB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/02_generalization-and-overfitting.mp4 29.5 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/05_what-about-data-science.mp4 29.3 MB
machine-learning-applied/04_machine-learning-projects/02_lesson-2-getting-ready-to-model/02_assessing-your-quam-for-use-in-your-business.mp4 29.2 MB
optimize-machine-learning-model-performance/02_responsible-machine-learning/01_feedback-fairness/02_positive-feedback-loops-negative-feedback-loops.mp4 28.8 MB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/03_why-you-need-to-set-up-a-data-pipeline.mp4 26.8 MB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/02_support-vector-machines/02_basics-of-support-vector-machines.mp4 26.6 MB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/02_deployment-issues-operational-processes/02_logging-ml-model-versioning.mp4 26.0 MB
machine-learning-classification-algorithms/04_contrasting-models/02_testing-and-validation-procedures/01_testing-your-models.mp4 25.0 MB
data-machine-learning/01_what-does-good-data-look-like/03_matchmaking/02_case-study-problem-from-data.mp4 25.0 MB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/04_gradient-descent.mp4 24.8 MB
optimize-machine-learning-model-performance/02_responsible-machine-learning/02_you-are-what-you-optimize-design-considerations/01_metric-design-observing-behaviours.mp4 24.8 MB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/03_infinite-feature-expansions/01_kernels.mp4 24.7 MB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/05_noise-and-sources-of-randomness.mp4 24.2 MB
data-machine-learning/04_bad-data/01_accept-limitations/04_bias-and-variance-tradeoff.mp4 24.2 MB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/02_simple-vs-expressive/02_bias-and-variance-tradeoff.mp4 24.2 MB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/01_technically-assessing-the-strength-of-your-quam.mp4 24.1 MB
optimize-machine-learning-model-performance/01_machine-learning-strategy/03_teamwork-communication/02_understanding-and-communicating-change.mp4 23.7 MB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/03_from-regression-to-classification/01_loss-for-classification.mp4 23.6 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/02_data-transformations.mp4 23.6 MB
data-machine-learning/01_what-does-good-data-look-like/03_matchmaking/04_weekly-summary-what-does-good-data-look-like.mp4 23.3 MB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/02_simple-vs-expressive/01_nonlinear-features-and-model-complexity.mp4 23.2 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/02_coordinate/02_types-of-data.mp4 23.2 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/02_classification-what-is-it-and-how-does-it-work.mp4 23.1 MB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/02_optimal-line-fitting.mp4 22.9 MB
optimize-machine-learning-model-performance/02_responsible-machine-learning/01_feedback-fairness/01_ai-4-good-for-all.mp4 22.9 MB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/02_simple-vs-expressive/03_regularizers.mp4 22.9 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/03_regression-fitting-lines-and-predicting-numbers.mp4 22.9 MB
optimize-machine-learning-model-performance/01_machine-learning-strategy/02_ownership-products/02_build-buy-partner.mp4 22.6 MB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/03_scaling-up/02_dashboard-essentials-metrics-monitoring.mp4 22.5 MB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/01_mapping-the-model-lifecycle/01_mlpl-recap.mp4 22.4 MB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/03_what-does-a-classifier-actually-do.mp4 22.1 MB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/01_what-are-decision-trees.mp4 22.0 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/01_the-three-kinds-of-machine-learning.mp4 21.8 MB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/02_metadata-matters.mp4 21.8 MB
data-machine-learning/04_bad-data/01_accept-limitations/01_imbalanced-data.mp4 21.5 MB
optimize-machine-learning-model-performance/02_responsible-machine-learning/02_you-are-what-you-optimize-design-considerations/02_secondary-effects-of-optimization.mp4 21.4 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/04_data-cleaning-everybodys-favourite-task.mp4 21.3 MB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/02_data-cleaning-everybodys-favourite-task.mp4 21.3 MB
data-machine-learning/04_bad-data/02_statistical-nuance/01_outliers.mp4 21.3 MB
data-machine-learning/03_feature-engineering-for-more-fun-profit/01_understanding-features/03_how-many-features.mp4 21.0 MB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/03_learning-curves.mp4 20.4 MB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/01_ethical-issues.mp4 20.2 MB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/03_scaling-up/01_separating-datastack-from-production.mp4 19.9 MB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/03_loss-and-convexity.mp4 19.6 MB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/03_risk-mitigation.mp4 19.6 MB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/02_identify-business-evaluation.mp4 19.5 MB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/01_line-fitting.mp4 19.2 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/02_converting-to-useful-forms.mp4 18.9 MB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/04_experimental-mindset.mp4 18.9 MB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/01_design-considerations/02_users-break-things.mp4 18.9 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/02_coordinate/03_aligning-similar-data.mp4 18.6 MB
data-machine-learning/01_what-does-good-data-look-like/03_matchmaking/01_identifying-data-from-problem.mp4 18.3 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/04_how-much-data-do-i-need.mp4 18.2 MB
machine-learning-applied/03_learning-data/01_lesson-1-data-needs/02_how-much-data-do-i-need.mp4 18.2 MB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/02_deployment-issues-operational-processes/01_when-do-i-retrain-the-model.mp4 18.2 MB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/03_communicating-technical-content/01_knowledge-transfer.mp4 17.9 MB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/02_ml-readiness.mp4 17.9 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/01_introduction-to-the-applied-machine-learning-specialization.mp4 17.8 MB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/02_quam-testing.mp4 17.8 MB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/01_design-considerations/01_integrating-info-systems.mp4 17.7 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/01_unsupervised-learning.mp4 17.7 MB
data-machine-learning/03_feature-engineering-for-more-fun-profit/02_building-good-features/01_what-is-unsupervised-learning.mp4 17.6 MB
optimize-machine-learning-model-performance/01_machine-learning-strategy/03_teamwork-communication/01_setting-up-a-team.mp4 17.5 MB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/03_everything-is-a-proxy.mp4 17.5 MB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/04_quam-updating.mp4 17.4 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/01_imputing-missing-values.mp4 17.3 MB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/03_communicating-technical-content/02_reporting-performance-to-stakeholders.mp4 17.1 MB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/02_support-vector-machines/01_hinge-loss.mp4 16.9 MB
machine-learning-classification-algorithms/04_contrasting-models/03_parameter-tuning/01_parameter-tuning-and-grid-search.mp4 16.4 MB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/03_quam-maintenance.mp4 15.9 MB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/01_design-considerations/03_time-space-complexity-in-production.mp4 15.9 MB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/01_quam-monitoring-and-logging.mp4 15.6 MB
optimize-machine-learning-model-performance/02_responsible-machine-learning/03_legalities-and-best-practices/01_regulatory-concerns.mp4 15.0 MB
machine-learning-applied/04_machine-learning-projects/01_lesson-1-machine-learning-process-lifecycle/01_mlpl-overview.mp4 14.8 MB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/01_broad-examples-narrowed-down.mp4 14.7 MB
machine-learning-classification-algorithms/04_contrasting-models/02_testing-and-validation-procedures/02_cross-validation.mp4 14.6 MB
optimize-machine-learning-model-performance/01_machine-learning-strategy/03_teamwork-communication/03_weekly-summary.mp4 14.6 MB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/01_image-classification-example.mp4 14.5 MB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/03_from-regression-to-classification/02_weekly-summary.mp4 14.3 MB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/04_no-free-lunch-theorem.mp4 13.8 MB
data-machine-learning/04_bad-data/03_consequences-of-bad-data/01_badness-multipliers.mp4 13.3 MB
data-machine-learning/03_feature-engineering-for-more-fun-profit/01_understanding-features/02_useful-useless-features.mp4 13.2 MB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/03_dealing-with-multimodal-data.mp4 13.2 MB
machine-learning-applied/02_machine-learning-in-the-real-world/02_lesson-2-applied-scenarios/03_what-to-consider-when-using-your-quam.mp4 13.2 MB
data-machine-learning/04_bad-data/01_accept-limitations/03_bias-in-data-sources.mp4 13.1 MB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/04_bias-in-data-sources.mp4 13.1 MB
optimize-machine-learning-model-performance/02_responsible-machine-learning/03_legalities-and-best-practices/02_weekly-summary.mp4 12.8 MB
machine-learning-applied/02_machine-learning-in-the-real-world/02_lesson-2-applied-scenarios/02_farmer-betty-and-her-precision-agriculture-plans.mp4 12.7 MB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/02_classification-assessment.mp4 12.4 MB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/03_weekly-summary.mp4 12.3 MB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/04_classification-in-scikit-learn.mp4 12.1 MB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/02_different-kinds-of-wrong.mp4 12.0 MB
machine-learning-classification-algorithms/04_contrasting-models/03_parameter-tuning/02_model-parameters.mp4 11.9 MB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/01_regression-assessment.mp4 11.6 MB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/04_weekly-summary.mp4 11.5 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/03_introduction-to-course-1.mp4 11.4 MB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/01_models-with-transfer-functions/01_logistic-regression.mp4 11.3 MB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/05_weekly-summary.mp4 11.2 MB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/01_introduction-to-the-course.mp4 11.1 MB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/03_communicating-technical-content/03_weekly-summary.mp4 10.7 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/03_data-quality.mp4 10.7 MB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/03_scaling-up/03_weekly-summary.mp4 10.2 MB
machine-learning-applied/03_learning-data/01_lesson-1-data-needs/01_sources-of-training-data.mp4 10.2 MB
data-machine-learning/03_feature-engineering-for-more-fun-profit/03_transfer-learning/03_weekly-summary-feature-engineering-for-more-fun-profit.mp4 9.8 MB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/03_infinite-feature-expansions/02_weekly-summary.mp4 9.5 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/08_fooling-neural-networks-supplemental_C1M1L1ReadingNeuralNetworks.pdf 9.5 MB
machine-learning-applied/04_machine-learning-projects/01_lesson-1-machine-learning-process-lifecycle/03_mlpl-as-experienced-by-farmer-betty.mp4 9.5 MB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/03_weekly-summary-preparing-your-data-for-machine-learning-success.mp4 9.5 MB
machine-learning-classification-algorithms/04_contrasting-models/03_parameter-tuning/03_weekly-summary.mp4 9.2 MB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/02_introduction-to-the-course.mp4 9.1 MB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/04_deep-learning-for-identifying-metastatic-breast-cancer-advanced-supplemental_DeepLearning_BIDMC.HMS.MIT_Camelyon_2016.pdf 8.8 MB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/01_introduction-to-the-course.mp4 7.9 MB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/04_weekly-summary.mp4 7.8 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/04_weekly-summary.mp4 7.6 MB
data-machine-learning/03_feature-engineering-for-more-fun-profit/02_building-good-features/03_feature-extraction.mp4 7.5 MB
data-machine-learning/04_bad-data/03_consequences-of-bad-data/03_weekly-summary-bad-data.mp4 6.7 MB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/02_instructor-introduction.mp4 5.8 MB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/01_math-review_Math_review.pdf 927.0 KB
machine-learning-applied/02_machine-learning-in-the-real-world/02_lesson-2-applied-scenarios/01_a-brief-introduction-into-precision-agriculture_Mulla_and_Khosla_2015.pdf 752.1 KB
machine-learning-classification-algorithms/05_Resources/02_more-techniques-for-hyperparameter-tuning/01__bergstra12a.pdf 711.2 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/05_learning-from-multiple-annotators-a-survey-supplemental_C1M2L2.pdf 687.5 KB
machine-learning-applied/04_machine-learning-projects/01_lesson-1-machine-learning-process-lifecycle/02_machine-learning-process-lifecycle-explained_MLPL.pdf 631.7 KB
machine-learning-applied/02_machine-learning-in-the-real-world/02_lesson-2-applied-scenarios/01_a-brief-introduction-into-precision-agriculture_instructions.html 498.6 KB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/04_some-resources-on-model-assessment-optional_model_evaluation.html 347.3 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/06_inferring-the-ground-truth-through-crowdsourcing-supplemental_C1M1L2B.pdf 166.1 KB
machine-learning-classification-algorithms/05_Resources/02_more-techniques-for-hyperparameter-tuning/01__Gini_index_fulltext.pdf 132.8 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/03_infinite-feature-expansions/03_scikitlearn-documentation-for-svms-optional_svm.html 102.5 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/03_scikitlearn-documentation-for-k-nearest-neighbours-optional_neighbors.html 99.8 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/04_scikitlearn-documentation-for-random-forests-optional_sklearn.ensemble.RandomForestClassifier.html 79.7 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/03_scikitlearn-documentation-for-decision-trees-optional_sklearn.tree.DecisionTreeClassifier.html 74.9 KB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/03_government-readings-on-data-privacy-supplemental_index.html 65.5 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/03_scikitlearn-documentation-for-decision-trees-optional_tree.html 61.9 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/05_scikitlearn-documentation-for-linear-regression-optional_sklearn.linear_model.LinearRegression.html 49.2 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/04_gradient-descent.en.srt 19.9 KB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/01_data-acquisition-and-understanding.en.srt 19.5 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/01_mapping-the-model-lifecycle/01_mlpl-recap.en.srt 18.8 KB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/03_business-understanding-and-problem-discovery.en.srt 17.7 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/01_classification-using-k-nearest-neighbours.en.srt 17.2 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/02_generalization-and-overfitting.en.srt 17.2 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/02_optimal-line-fitting.en.srt 16.8 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/03_transfer-learning/01_transfer-learning.en.srt 16.4 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/01_models-with-transfer-functions/02_neural-networks.en.srt 16.2 KB
machine-learning-classification-algorithms/04_contrasting-models/02_testing-and-validation-procedures/01_testing-your-models.en.srt 16.0 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/01_imputing-missing-values.en.srt 15.9 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/01_data-warehousing.en.srt 15.6 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/03_teamwork-communication/02_understanding-and-communicating-change.en.srt 15.3 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/03_from-regression-to-classification/01_loss-for-classification.en.srt 15.2 KB
data-machine-learning/01_what-does-good-data-look-like/03_matchmaking/02_case-study-problem-from-data.en.srt 14.9 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/02_distance-measures.en.srt 14.7 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/01_understanding-features/01_what-are-the-simplest-features-to-try.en.srt 14.3 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/02_converting-to-useful-forms.en.srt 14.2 KB
data-machine-learning/04_bad-data/01_accept-limitations/04_bias-and-variance-tradeoff.en.srt 13.9 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/02_simple-vs-expressive/02_bias-and-variance-tradeoff.en.srt 13.9 KB
data-machine-learning/04_bad-data/01_accept-limitations/01_imbalanced-data.en.srt 13.8 KB
data-machine-learning/04_bad-data/02_statistical-nuance/02_skewed-distributions.en.srt 13.4 KB
data-machine-learning/04_bad-data/03_consequences-of-bad-data/02_live-data-danger.en.srt 13.3 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/01_what-are-decision-trees.en.srt 13.2 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/03_infinite-feature-expansions/01_kernels.en.srt 13.2 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/02_building-good-features/02_feature-selection.en.srt 13.0 KB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/05_exploring-the-process-of-problem-definition.en.srt 13.0 KB
machine-learning-applied/04_machine-learning-projects/02_lesson-2-getting-ready-to-model/01_exploring-the-process-of-problem-definition.en.srt 13.0 KB
machine-learning-applied/04_machine-learning-projects/02_lesson-2-getting-ready-to-model/02_assessing-your-quam-for-use-in-your-business.en.srt 12.9 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/03_loss-and-convexity.en.srt 12.8 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/02_support-vector-machines/01_hinge-loss.en.srt 12.8 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/02_coordinate/01_everything-has-to-be-numbers.en.srt 12.7 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/02_simple-vs-expressive/01_nonlinear-features-and-model-complexity.en.srt 12.7 KB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/02_classification-assessment.en.srt 12.4 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/02_data-transformations.en.srt 12.3 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/02_ownership-products/02_build-buy-partner.en.srt 12.2 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/01_line-fitting.en.srt 12.1 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/02_support-vector-machines/02_basics-of-support-vector-machines.en.srt 12.1 KB
machine-learning-classification-algorithms/04_contrasting-models/03_parameter-tuning/01_parameter-tuning-and-grid-search.en.srt 12.1 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/01_feedback-fairness/02_positive-feedback-loops-negative-feedback-loops.en.srt 12.0 KB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/04_features-and-transformations-of-raw-data.en.srt 11.9 KB
machine-learning-applied/02_machine-learning-in-the-real-world/01_lesson-1-machines-are-different-from-humans/02_features-and-transformations-of-raw-data.en.srt 11.9 KB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/02_metadata-matters.en.srt 11.8 KB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/01_technically-assessing-the-strength-of-your-quam.en.srt 11.5 KB
data-machine-learning/04_bad-data/02_statistical-nuance/01_outliers.en.srt 11.3 KB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/03_business-understanding-and-problem-discovery.en.txt 11.1 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/03_reinforcement-learning.en.srt 11.0 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/03_what-does-a-classifier-actually-do.en.srt 11.0 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/02_ml-readiness.en.srt 10.9 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/03_teamwork-communication/01_setting-up-a-team.en.srt 10.9 KB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/04_no-free-lunch-theorem.en.srt 10.8 KB
machine-learning-classification-algorithms/04_contrasting-models/03_parameter-tuning/02_model-parameters.en.srt 10.8 KB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/03_learning-curves.en.srt 10.8 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/03_data-quality.en.srt 10.7 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/01_understanding-features/02_useful-useless-features.en.srt 10.7 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/02_building-good-features/01_what-is-unsupervised-learning.en.srt 10.6 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/02_simple-vs-expressive/03_regularizers.en.srt 10.6 KB
data-machine-learning/04_bad-data/01_accept-limitations/02_generalization-and-how-machines-actually-learn.en.srt 10.5 KB
machine-learning-applied/02_machine-learning-in-the-real-world/01_lesson-1-machines-are-different-from-humans/01_generalization-and-how-machines-actually-learn.en.srt 10.5 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/02_quam-testing.en.srt 10.5 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/04_quam-updating.en.srt 10.4 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/03_risk-mitigation.en.srt 10.4 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/04_gradient-descent.en.txt 10.4 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/01_mapping-the-model-lifecycle/02_post-deployment-challenges.en.srt 10.3 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/01_quam-monitoring-and-logging.en.srt 10.3 KB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/01_data-acquisition-and-understanding.en.txt 10.3 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/02_building-good-features/04_possibilities-for-text-features_instructions.html 10.3 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/01_models-with-transfer-functions/02_neural-networks.en.txt 10.2 KB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/01_regression-assessment.en.srt 10.1 KB
machine-learning-classification-algorithms/04_contrasting-models/02_testing-and-validation-procedures/02_cross-validation.en.srt 10.1 KB
data-machine-learning/01_what-does-good-data-look-like/03_matchmaking/01_identifying-data-from-problem.en.srt 10.0 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/01_mapping-the-model-lifecycle/01_mlpl-recap.en.txt 10.0 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/02_coordinate/02_types-of-data.en.srt 9.7 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/04_what-is-artificial-intelligence-and-machine-learning.en.srt 9.7 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/02_you-are-what-you-optimize-design-considerations/01_metric-design-observing-behaviours.en.srt 9.5 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/02_distance-measures.en.txt 9.5 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/03_scaling-up/02_dashboard-essentials-metrics-monitoring.en.srt 9.1 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/01_understanding-features/01_what-are-the-simplest-features-to-try.en.txt 9.1 KB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/01_broad-examples-narrowed-down.en.srt 9.1 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/02_generalization-and-overfitting.en.txt 9.0 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/01_classification-using-k-nearest-neighbours.en.txt 9.0 KB
data-machine-learning/01_what-does-good-data-look-like/03_matchmaking/04_weekly-summary-what-does-good-data-look-like.en.srt 9.0 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/06_the-machine-learning-process.en.srt 9.0 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/02_optimal-line-fitting.en.txt 9.0 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/01_understanding-features/03_how-many-features.en.srt 8.8 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/03_transfer-learning/01_transfer-learning.en.txt 8.7 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/01_design-considerations/03_time-space-complexity-in-production.en.srt 8.7 KB
machine-learning-classification-algorithms/04_contrasting-models/02_testing-and-validation-procedures/01_testing-your-models.en.txt 8.6 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/04_data-cleaning-everybodys-favourite-task.en.srt 8.6 KB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/02_data-cleaning-everybodys-favourite-task.en.srt 8.6 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/04_experimental-mindset.en.srt 8.5 KB
machine-learning-applied/04_machine-learning-projects/01_lesson-1-machine-learning-process-lifecycle/01_mlpl-overview.en.srt 8.5 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/01_imputing-missing-values.en.txt 8.5 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/01_feedback-fairness/01_ai-4-good-for-all.en.srt 8.5 KB
data-machine-learning/04_bad-data/02_statistical-nuance/02_skewed-distributions.en.txt 8.5 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/03_from-regression-to-classification/02_weekly-summary.en.srt 8.4 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/01_design-considerations/01_integrating-info-systems.en.srt 8.4 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/02_deployment-issues-operational-processes/01_when-do-i-retrain-the-model.en.srt 8.4 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/02_building-good-features/02_feature-selection.en.txt 8.3 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/01_data-warehousing.en.txt 8.2 KB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/02_identify-business-evaluation.en.srt 8.2 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/02_coordinate/03_aligning-similar-data.en.srt 8.2 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/03_loss-and-convexity.en.txt 8.1 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/02_deployment-issues-operational-processes/02_logging-ml-model-versioning.en.srt 8.1 KB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/05_exploring-the-process-of-problem-definition.en.txt 8.1 KB
machine-learning-applied/04_machine-learning-projects/02_lesson-2-getting-ready-to-model/01_exploring-the-process-of-problem-definition.en.txt 8.1 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/03_teamwork-communication/02_understanding-and-communicating-change.en.txt 8.1 KB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/03_why-you-need-to-set-up-a-data-pipeline.en.srt 8.1 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/03_from-regression-to-classification/01_loss-for-classification.en.txt 8.1 KB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/03_everything-is-a-proxy.en.srt 8.0 KB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/05_noise-and-sources-of-randomness.en.srt 7.9 KB
data-machine-learning/01_what-does-good-data-look-like/03_matchmaking/02_case-study-problem-from-data.en.txt 7.9 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/02_ownership-products/02_build-buy-partner.en.txt 7.9 KB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/01_ethical-issues.en.srt 7.9 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/04_classification-in-scikit-learn.en.srt 7.8 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/02_data-transformations.en.txt 7.8 KB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/02_different-kinds-of-wrong.en.srt 7.6 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/03_communicating-technical-content/01_knowledge-transfer.en.srt 7.6 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/04_how-much-data-do-i-need.en.srt 7.6 KB
machine-learning-applied/03_learning-data/01_lesson-1-data-needs/02_how-much-data-do-i-need.en.srt 7.6 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/03_legalities-and-best-practices/01_regulatory-concerns.en.srt 7.5 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/02_converting-to-useful-forms.en.txt 7.4 KB
data-machine-learning/04_bad-data/01_accept-limitations/01_imbalanced-data.en.txt 7.4 KB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/05_understanding-machine-learning-projects_exam.html 7.4 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/03_scaling-up/01_separating-datastack-from-production.en.srt 7.3 KB
data-machine-learning/04_bad-data/03_consequences-of-bad-data/01_badness-multipliers.en.srt 7.3 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/02_ownership-products/01_ip-questions_instructions.html 7.3 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/01_unsupervised-learning.en.srt 7.3 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/03_communicating-technical-content/02_reporting-performance-to-stakeholders.en.srt 7.3 KB
data-machine-learning/04_bad-data/01_accept-limitations/04_bias-and-variance-tradeoff.en.txt 7.2 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/02_simple-vs-expressive/02_bias-and-variance-tradeoff.en.txt 7.2 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/02_ml-readiness.en.txt 7.1 KB
data-machine-learning/04_bad-data/03_consequences-of-bad-data/02_live-data-danger.en.txt 7.1 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/01_understanding-features/02_useful-useless-features.en.txt 7.0 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/01_what-are-decision-trees.en.txt 7.0 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/03_quam-maintenance.en.srt 7.0 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/03_infinite-feature-expansions/01_kernels.en.txt 7.0 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/05_what-about-data-science.en.srt 6.9 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/01_design-considerations/02_users-break-things.en.srt 6.9 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/02_classification-what-is-it-and-how-does-it-work.en.srt 6.9 KB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/03_learning-curves.en.txt 6.9 KB
machine-learning-applied/04_machine-learning-projects/02_lesson-2-getting-ready-to-model/02_assessing-your-quam-for-use-in-your-business.en.txt 6.9 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/02_you-are-what-you-optimize-design-considerations/02_secondary-effects-of-optimization.en.srt 6.8 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/02_coordinate/01_everything-has-to-be-numbers.en.txt 6.8 KB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/05_understanding-data-for-ml_exam.html 6.8 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/02_simple-vs-expressive/01_nonlinear-features-and-model-complexity.en.txt 6.8 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/02_support-vector-machines/01_hinge-loss.en.txt 6.8 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/01_mapping-the-model-lifecycle/02_post-deployment-challenges.en.txt 6.8 KB
machine-learning-applied/04_machine-learning-projects/01_lesson-1-machine-learning-process-lifecycle/03_mlpl-as-experienced-by-farmer-betty.en.srt 6.8 KB
machine-learning-classification-algorithms/04_contrasting-models/03_parameter-tuning/02_model-parameters.en.txt 6.7 KB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/02_classification-assessment.en.txt 6.7 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/01_models-with-transfer-functions/01_logistic-regression.en.srt 6.7 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/02_building-good-features/01_what-is-unsupervised-learning.en.txt 6.6 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/03_regression-fitting-lines-and-predicting-numbers.en.srt 6.6 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/03_transfer-learning/02_word-embeddings_instructions.html 6.5 KB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/01_regression-assessment.en.txt 6.5 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/01_feedback-fairness/02_positive-feedback-loops-negative-feedback-loops.en.txt 6.4 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/01_line-fitting.en.txt 6.4 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/02_support-vector-machines/02_basics-of-support-vector-machines.en.txt 6.4 KB
machine-learning-classification-algorithms/04_contrasting-models/03_parameter-tuning/01_parameter-tuning-and-grid-search.en.txt 6.4 KB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/02_metadata-matters.en.txt 6.4 KB
data-machine-learning/01_what-does-good-data-look-like/03_matchmaking/01_identifying-data-from-problem.en.txt 6.3 KB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/04_features-and-transformations-of-raw-data.en.txt 6.3 KB
machine-learning-applied/02_machine-learning-in-the-real-world/01_lesson-1-machines-are-different-from-humans/02_features-and-transformations-of-raw-data.en.txt 6.3 KB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/06_machine-learning-in-the-real-world-review_exam.html 6.3 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/02_you-are-what-you-optimize-design-considerations/01_metric-design-observing-behaviours.en.txt 6.3 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/05_identifying-machine-learning-techniques_exam.html 6.2 KB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/01_technically-assessing-the-strength-of-your-quam.en.txt 6.2 KB
data-machine-learning/01_what-does-good-data-look-like/03_matchmaking/03_match-data-to-the-needs-of-the-learning-algorithm_instructions.html 6.1 KB
data-machine-learning/04_bad-data/02_statistical-nuance/01_outliers.en.txt 6.1 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/04_weekly-summary.en.srt 6.0 KB
data-machine-learning/04_bad-data/01_accept-limitations/03_bias-in-data-sources.en.srt 6.0 KB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/04_bias-in-data-sources.en.srt 6.0 KB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/01_image-classification-example.en.srt 5.9 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/03_scaling-up/02_dashboard-essentials-metrics-monitoring.en.txt 5.9 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/03_what-does-a-classifier-actually-do.en.txt 5.8 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/03_reinforcement-learning.en.txt 5.8 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/03_teamwork-communication/01_setting-up-a-team.en.txt 5.8 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/01_introduction-to-the-applied-machine-learning-specialization.en.srt 5.8 KB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/04_no-free-lunch-theorem.en.txt 5.8 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/01_understanding-features/03_how-many-features.en.txt 5.8 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/02_simple-vs-expressive/03_regularizers.en.txt 5.7 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/03_data-quality.en.txt 5.7 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/01_design-considerations/03_time-space-complexity-in-production.en.txt 5.6 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/02_quam-testing.en.txt 5.6 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/03_risk-mitigation.en.txt 5.5 KB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/03_dealing-with-multimodal-data.en.srt 5.5 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/01_the-three-kinds-of-machine-learning.en.srt 5.5 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/04_quam-updating.en.txt 5.5 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/01_quam-monitoring-and-logging.en.txt 5.5 KB
data-machine-learning/04_bad-data/01_accept-limitations/02_generalization-and-how-machines-actually-learn.en.txt 5.5 KB
machine-learning-applied/02_machine-learning-in-the-real-world/01_lesson-1-machines-are-different-from-humans/01_generalization-and-how-machines-actually-learn.en.txt 5.5 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/04_experimental-mindset.en.txt 5.5 KB
machine-learning-applied/04_machine-learning-projects/01_lesson-1-machine-learning-process-lifecycle/01_mlpl-overview.en.txt 5.4 KB
machine-learning-classification-algorithms/04_contrasting-models/02_testing-and-validation-procedures/02_cross-validation.en.txt 5.4 KB
machine-learning-applied/02_machine-learning-in-the-real-world/02_lesson-2-applied-scenarios/02_farmer-betty-and-her-precision-agriculture-plans.en.srt 5.3 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/02_deployment-issues-operational-processes/01_when-do-i-retrain-the-model.en.txt 5.3 KB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/03_weekly-summary.en.srt 5.3 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/02_coordinate/03_aligning-similar-data.en.txt 5.3 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/02_deployment-issues-operational-processes/02_logging-ml-model-versioning.en.txt 5.2 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/02_coordinate/02_types-of-data.en.txt 5.2 KB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/03_why-you-need-to-set-up-a-data-pipeline.en.txt 5.1 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/04_what-is-artificial-intelligence-and-machine-learning.en.txt 5.1 KB
machine-learning-applied/03_learning-data/01_lesson-1-data-needs/01_sources-of-training-data.en.srt 5.1 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/03_communicating-technical-content/01_knowledge-transfer.en.txt 5.0 KB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/05_noise-and-sources-of-randomness.en.txt 5.0 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/09_concepts-and-definitions_exam.html 4.9 KB
data-machine-learning/01_what-does-good-data-look-like/03_matchmaking/04_weekly-summary-what-does-good-data-look-like.en.txt 4.8 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/02_building-good-features/03_feature-extraction.en.srt 4.8 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/06_the-machine-learning-process.en.txt 4.7 KB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/01_broad-examples-narrowed-down.en.txt 4.7 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/03_communicating-technical-content/02_reporting-performance-to-stakeholders.en.txt 4.7 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/04_data-cleaning-everybodys-favourite-task.en.txt 4.7 KB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/02_data-cleaning-everybodys-favourite-task.en.txt 4.7 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/03_from-regression-to-classification/02_weekly-summary.en.txt 4.6 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/01_feedback-fairness/01_ai-4-good-for-all.en.txt 4.6 KB
data-machine-learning/04_bad-data/03_consequences-of-bad-data/01_badness-multipliers.en.txt 4.5 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/01_design-considerations/01_integrating-info-systems.en.txt 4.5 KB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/02_identify-business-evaluation.en.txt 4.4 KB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/03_everything-is-a-proxy.en.txt 4.3 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/02_you-are-what-you-optimize-design-considerations/02_secondary-effects-of-optimization.en.txt 4.3 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/01_models-with-transfer-functions/01_logistic-regression.en.txt 4.3 KB
machine-learning-applied/02_machine-learning-in-the-real-world/02_lesson-2-applied-scenarios/03_what-to-consider-when-using-your-quam.en.srt 4.3 KB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/01_ethical-issues.en.txt 4.3 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/04_classification-in-scikit-learn.en.txt 4.1 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/03_teamwork-communication/03_weekly-summary.en.srt 4.1 KB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/02_different-kinds-of-wrong.en.txt 4.0 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/03_legalities-and-best-practices/01_regulatory-concerns.en.txt 4.0 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/03_legalities-and-best-practices/02_weekly-summary.en.srt 4.0 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/01_consolidate-sources/04_how-much-data-do-i-need.en.txt 4.0 KB
machine-learning-applied/03_learning-data/01_lesson-1-data-needs/02_how-much-data-do-i-need.en.txt 4.0 KB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/05_weekly-summary.en.srt 4.0 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/03_scaling-up/01_separating-datastack-from-production.en.txt 3.9 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/03_introduction-to-course-1.en.srt 3.9 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/01_unsupervised-learning.en.txt 3.9 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/02_maintenance-checkpoints/03_quam-maintenance.en.txt 3.8 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/05_what-about-data-science.en.txt 3.8 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/01_introduction-to-the-applied-machine-learning-specialization.en.txt 3.8 KB
machine-learning-applied/02_machine-learning-in-the-real-world/02_lesson-2-applied-scenarios/05_data-is-central-to-your-ml-problem-required_instructions.html 3.8 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/01_design-considerations/02_users-break-things.en.txt 3.7 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/03_scaling-up/03_weekly-summary.en.srt 3.7 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/01_introduction-to-the-course.en.srt 3.6 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/02_classification-what-is-it-and-how-does-it-work.en.txt 3.6 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/03_transfer-learning/03_weekly-summary-feature-engineering-for-more-fun-profit.en.srt 3.6 KB
machine-learning-applied/04_machine-learning-projects/01_lesson-1-machine-learning-process-lifecycle/03_mlpl-as-experienced-by-farmer-betty.en.txt 3.6 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/03_regression-fitting-lines-and-predicting-numbers.en.txt 3.6 KB
machine-learning-classification-algorithms/04_contrasting-models/03_parameter-tuning/03_weekly-summary.en.srt 3.4 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/03_infinite-feature-expansions/02_weekly-summary.en.srt 3.4 KB
machine-learning-applied/02_machine-learning-in-the-real-world/02_lesson-2-applied-scenarios/02_farmer-betty-and-her-precision-agriculture-plans.en.txt 3.4 KB
machine-learning-applied/02_machine-learning-in-the-real-world/02_lesson-2-applied-scenarios/04_farmer-betty-tried-unsupervised-learning-required_instructions.html 3.3 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/03_communicating-technical-content/03_weekly-summary.en.srt 3.3 KB
machine-learning-applied/03_learning-data/01_lesson-1-data-needs/01_sources-of-training-data.en.txt 3.2 KB
data-machine-learning/04_bad-data/01_accept-limitations/03_bias-in-data-sources.en.txt 3.2 KB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/04_bias-in-data-sources.en.txt 3.2 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/04_weekly-summary.en.txt 3.2 KB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/01_image-classification-example.en.txt 3.2 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/04_weekly-summary.en.srt 3.1 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/03_weekly-summary-preparing-your-data-for-machine-learning-success.en.srt 3.1 KB
data-machine-learning/01_what-does-good-data-look-like/02_know-your-data/03_dealing-with-multimodal-data.en.txt 3.0 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/01_the-three-kinds-of-machine-learning.en.txt 3.0 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/02_introduction-to-the-course.en.srt 3.0 KB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/03_weekly-summary.en.txt 2.8 KB
machine-learning-applied/02_machine-learning-in-the-real-world/02_lesson-2-applied-scenarios/03_what-to-consider-when-using-your-quam.en.txt 2.7 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/03_teamwork-communication/03_weekly-summary.en.txt 2.6 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/02_building-good-features/03_feature-extraction.en.txt 2.6 KB
optimize-machine-learning-model-performance/02_responsible-machine-learning/03_legalities-and-best-practices/02_weekly-summary.en.txt 2.5 KB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/02_machine-learning-process-lifecycle-review_instructions.html 2.4 KB
data-machine-learning/04_bad-data/03_consequences-of-bad-data/03_weekly-summary-bad-data.en.srt 2.4 KB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/04_weekly-summary.en.srt 2.4 KB
optimize-machine-learning-model-performance/01_machine-learning-strategy/01_planning/01_introduction-to-the-course.en.txt 2.3 KB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/01_introduction-to-the-course.en.srt 2.3 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/02_semi-supervised-learning-required_instructions.html 2.2 KB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/05_weekly-summary.en.txt 2.2 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/03_introduction-to-course-1.en.txt 2.1 KB
optimize-machine-learning-model-performance/03_machine-learning-in-production-planning/03_communicating-technical-content/03_weekly-summary.en.txt 2.1 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/07_what-about-deep-learning-supplemental_instructions.html 2.0 KB
optimize-machine-learning-model-performance/04_care-and-feeding-of-your-machine-learning-system/03_scaling-up/03_weekly-summary.en.txt 2.0 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/08_fooling-neural-networks-supplemental_instructions.html 2.0 KB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/03_government-readings-on-data-privacy-supplemental_instructions.html 2.0 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/02_instructor-introduction.en.srt 2.0 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/02_introduction-to-the-course.en.txt 1.9 KB
data-machine-learning/03_feature-engineering-for-more-fun-profit/03_transfer-learning/03_weekly-summary-feature-engineering-for-more-fun-profit.en.txt 1.9 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/03_infinite-feature-expansions/02_weekly-summary.en.txt 1.9 KB
data-machine-learning/02_preparing-your-data-for-machine-learning-success/03_clean-complete/03_weekly-summary-preparing-your-data-for-machine-learning-success.en.txt 1.9 KB
machine-learning-classification-algorithms/04_contrasting-models/03_parameter-tuning/03_weekly-summary.en.txt 1.9 KB
machine-learning-classification-algorithms/05_Resources/02_more-techniques-for-hyperparameter-tuning/01__resources.html 1.7 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/03_lesson-3-broader-machine-learning/04_weekly-summary.en.txt 1.7 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/04_how-to-curate-a-ground-truth-for-your-business-dataset-required_instructions.html 1.6 KB
machine-learning-applied/03_learning-data/03_lesson-3-data-process/04_weekly-summary.en.txt 1.5 KB
machine-learning-classification-algorithms/04_contrasting-models/01_model-assessment/04_some-resources-on-model-assessment-optional_instructions.html 1.5 KB
machine-learning-applied/03_learning-data/02_lesson-2-data-relates-to-problems/02_data-protection-laws-required_instructions.html 1.5 KB
data-machine-learning/01_what-does-good-data-look-like/01_know-your-problem/01_introduction-to-the-course.en.txt 1.5 KB
machine-learning-applied/02_machine-learning-in-the-real-world/03_lesson-3-getting-good-questions/04_martin-zinkevichs-rules-for-ml-supplemental_instructions.html 1.4 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/03_scikitlearn-documentation-for-decision-trees-optional_instructions.html 1.4 KB
machine-learning-applied/04_machine-learning-projects/03_lesson-3-model-learning-and-evaluation/04_deep-learning-for-identifying-metastatic-breast-cancer-advanced-supplemental_instructions.html 1.3 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/06_inferring-the-ground-truth-through-crowdsourcing-supplemental_instructions.html 1.3 KB
machine-learning-classification-algorithms/05_Resources/01_svm-a-short-introduction-to-duality/01__resources.html 1.3 KB
data-machine-learning/04_bad-data/03_consequences-of-bad-data/03_weekly-summary-bad-data.en.txt 1.3 KB
machine-learning-classification-algorithms/02_functions-for-fun-and-profit/01_finding-lines/05_scikitlearn-documentation-for-linear-regression-optional_instructions.html 1.2 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/02_decision-trees/04_scikitlearn-documentation-for-random-forests-optional_instructions.html 1.2 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/02_lesson-2-supervised-learning/05_learning-from-multiple-annotators-a-survey-supplemental_instructions.html 1.2 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/03_k-nearest-neighbours/03_scikitlearn-documentation-for-k-nearest-neighbours-optional_instructions.html 1.1 KB
machine-learning-applied/01_introduction-to-machine-learning-applications/01_lesson-1-definitions/02_instructor-introduction.en.txt 1.1 KB
machine-learning-classification-algorithms/03_regression-for-classification-support-vector-machines/03_infinite-feature-expansions/03_scikitlearn-documentation-for-svms-optional_instructions.html 1.1 KB
machine-learning-classification-algorithms/01_classification-using-decision-trees-and-k-nn/01_classification-in-a-nutshell/01_math-review_instructions.html 1.1 KB
machine-learning-applied/04_machine-learning-projects/01_lesson-1-machine-learning-process-lifecycle/02_machine-learning-process-lifecycle-explained_instructions.html 1.0 KB
0. Websites you may like/[CourserHub.com].url 123 bytes
machine-learning-classification-algorithms/0. Websites you may like/[CourserHub.com].url 123 bytes
Readme.txt 51 bytes
machine-learning-classification-algorithms/Readme.txt 51 bytes

[CourserHub.com] Coursera - Machine Learning Algorithms in the Real World Specialization

File List

Download Torrent

Related Resources

Copyright Infringement