CourserHub com Coursera Machine Learning Algorithms in the Real World Specialization

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[CourserHub.com].url

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Readme.txt

/.../01_know-your-problem/

01_introduction-to-the-course.en.srt

01_introduction-to-the-course.en.txt

01_introduction-to-the-course.mp4

02_machine-learning-process-lifecycle-review_instructions.html

03_business-understanding-and-problem-discovery.en.srt

03_business-understanding-and-problem-discovery.en.txt

03_business-understanding-and-problem-discovery.mp4

04_no-free-lunch-theorem.en.srt

04_no-free-lunch-theorem.en.txt

04_no-free-lunch-theorem.mp4

05_exploring-the-process-of-problem-definition.en.srt

05_exploring-the-process-of-problem-definition.en.txt

05_exploring-the-process-of-problem-definition.mp4

/.../02_know-your-data/

01_data-acquisition-and-understanding.en.srt

01_data-acquisition-and-understanding.en.txt

01_data-acquisition-and-understanding.mp4

02_metadata-matters.en.srt

02_metadata-matters.en.txt

02_metadata-matters.mp4

03_dealing-with-multimodal-data.en.srt

03_dealing-with-multimodal-data.en.txt

03_dealing-with-multimodal-data.mp4

04_features-and-transformations-of-raw-data.en.srt

04_features-and-transformations-of-raw-data.en.txt

04_features-and-transformations-of-raw-data.mp4

/.../01_what-does-good-data-look-like/03_matchmaking/

01_identifying-data-from-problem.en.srt

01_identifying-data-from-problem.en.txt

01_identifying-data-from-problem.mp4

02_case-study-problem-from-data.en.srt

02_case-study-problem-from-data.en.txt

02_case-study-problem-from-data.mp4

03_match-data-to-the-needs-of-the-learning-algorithm_instructions.html

04_weekly-summary-what-does-good-data-look-like.en.srt

04_weekly-summary-what-does-good-data-look-like.en.txt

04_weekly-summary-what-does-good-data-look-like.mp4

/.../01_consolidate-sources/

01_data-warehousing.en.srt

01_data-warehousing.en.txt

01_data-warehousing.mp4

02_converting-to-useful-forms.en.srt

02_converting-to-useful-forms.en.txt

02_converting-to-useful-forms.mp4

03_data-quality.en.srt

03_data-quality.en.txt

03_data-quality.mp4

04_how-much-data-do-i-need.en.srt

04_how-much-data-do-i-need.en.txt

04_how-much-data-do-i-need.mp4

/.../02_preparing-your-data-for-machine-learning-success/02_coordinate/

01_everything-has-to-be-numbers.en.srt

01_everything-has-to-be-numbers.en.txt

01_everything-has-to-be-numbers.mp4

02_types-of-data.en.srt

02_types-of-data.en.txt

02_types-of-data.mp4

03_aligning-similar-data.en.srt

03_aligning-similar-data.en.txt

03_aligning-similar-data.mp4

/.../03_clean-complete/

01_imputing-missing-values.en.srt

01_imputing-missing-values.en.txt

01_imputing-missing-values.mp4

02_data-transformations.en.srt

02_data-transformations.en.txt

02_data-transformations.mp4

03_weekly-summary-preparing-your-data-for-machine-learning-success.en.srt

03_weekly-summary-preparing-your-data-for-machine-learning-success.en.txt

03_weekly-summary-preparing-your-data-for-machine-learning-success.mp4

04_data-cleaning-everybodys-favourite-task.en.srt

04_data-cleaning-everybodys-favourite-task.en.txt

04_data-cleaning-everybodys-favourite-task.mp4

/.../01_understanding-features/

01_what-are-the-simplest-features-to-try.en.srt

01_what-are-the-simplest-features-to-try.en.txt

01_what-are-the-simplest-features-to-try.mp4

02_useful-useless-features.en.srt

02_useful-useless-features.en.txt

02_useful-useless-features.mp4

03_how-many-features.en.srt

03_how-many-features.en.txt

03_how-many-features.mp4

/.../02_building-good-features/

01_what-is-unsupervised-learning.en.srt

01_what-is-unsupervised-learning.en.txt

01_what-is-unsupervised-learning.mp4

02_feature-selection.en.srt

02_feature-selection.en.txt

02_feature-selection.mp4

03_feature-extraction.en.srt

03_feature-extraction.en.txt

03_feature-extraction.mp4

04_possibilities-for-text-features_instructions.html

/.../03_transfer-learning/

01_transfer-learning.en.srt

01_transfer-learning.en.txt

01_transfer-learning.mp4

02_word-embeddings_instructions.html

03_weekly-summary-feature-engineering-for-more-fun-profit.en.srt

03_weekly-summary-feature-engineering-for-more-fun-profit.en.txt

03_weekly-summary-feature-engineering-for-more-fun-profit.mp4

/.../01_accept-limitations/

01_imbalanced-data.en.srt

01_imbalanced-data.en.txt

01_imbalanced-data.mp4

02_generalization-and-how-machines-actually-learn.en.srt

02_generalization-and-how-machines-actually-learn.en.txt

02_generalization-and-how-machines-actually-learn.mp4

03_bias-in-data-sources.en.srt

03_bias-in-data-sources.en.txt

03_bias-in-data-sources.mp4

04_bias-and-variance-tradeoff.en.srt

04_bias-and-variance-tradeoff.en.txt

04_bias-and-variance-tradeoff.mp4

/.../02_statistical-nuance/

01_outliers.en.srt

01_outliers.en.txt

01_outliers.mp4

02_skewed-distributions.en.srt

02_skewed-distributions.en.txt

02_skewed-distributions.mp4

/.../03_consequences-of-bad-data/

01_badness-multipliers.en.srt

01_badness-multipliers.en.txt

01_badness-multipliers.mp4

02_live-data-danger.en.srt

02_live-data-danger.en.txt

02_live-data-danger.mp4

03_weekly-summary-bad-data.en.srt

03_weekly-summary-bad-data.en.txt

03_weekly-summary-bad-data.mp4

/.../01_lesson-1-definitions/

01_introduction-to-the-applied-machine-learning-specialization.en.srt

01_introduction-to-the-applied-machine-learning-specialization.en.txt

01_introduction-to-the-applied-machine-learning-specialization.mp4

02_instructor-introduction.en.srt

02_instructor-introduction.en.txt

02_instructor-introduction.mp4

03_introduction-to-course-1.en.srt

03_introduction-to-course-1.en.txt

03_introduction-to-course-1.mp4

04_what-is-artificial-intelligence-and-machine-learning.en.srt

04_what-is-artificial-intelligence-and-machine-learning.en.txt

04_what-is-artificial-intelligence-and-machine-learning.mp4

05_what-about-data-science.en.srt

05_what-about-data-science.en.txt

05_what-about-data-science.mp4

06_the-machine-learning-process.en.srt

06_the-machine-learning-process.en.txt

06_the-machine-learning-process.mp4

07_what-about-deep-learning-supplemental_instructions.html

08_fooling-neural-networks-supplemental_C1M1L1ReadingNeuralNetworks.pdf

08_fooling-neural-networks-supplemental_instructions.html

09_concepts-and-definitions_exam.html

/.../02_lesson-2-supervised-learning/

01_the-three-kinds-of-machine-learning.en.srt

01_the-three-kinds-of-machine-learning.en.txt

01_the-three-kinds-of-machine-learning.mp4

02_classification-what-is-it-and-how-does-it-work.en.srt

02_classification-what-is-it-and-how-does-it-work.en.txt

02_classification-what-is-it-and-how-does-it-work.mp4

03_regression-fitting-lines-and-predicting-numbers.en.srt

03_regression-fitting-lines-and-predicting-numbers.en.txt

03_regression-fitting-lines-and-predicting-numbers.mp4

04_how-to-curate-a-ground-truth-for-your-business-dataset-required_instructions.html

05_learning-from-multiple-annotators-a-survey-supplemental_C1M2L2.pdf

05_learning-from-multiple-annotators-a-survey-supplemental_instructions.html

06_inferring-the-ground-truth-through-crowdsourcing-supplemental_C1M1L2B.pdf

06_inferring-the-ground-truth-through-crowdsourcing-supplemental_instructions.html

/.../03_lesson-3-broader-machine-learning/

01_unsupervised-learning.en.srt

01_unsupervised-learning.en.txt

01_unsupervised-learning.mp4

02_semi-supervised-learning-required_instructions.html

03_reinforcement-learning.en.srt

03_reinforcement-learning.en.txt

03_reinforcement-learning.mp4

04_weekly-summary.en.srt

04_weekly-summary.en.txt

04_weekly-summary.mp4

05_identifying-machine-learning-techniques_exam.html

/.../01_lesson-1-machines-are-different-from-humans/

01_generalization-and-how-machines-actually-learn.en.srt

01_generalization-and-how-machines-actually-learn.en.txt

01_generalization-and-how-machines-actually-learn.mp4

02_features-and-transformations-of-raw-data.en.srt

02_features-and-transformations-of-raw-data.en.txt

02_features-and-transformations-of-raw-data.mp4

/.../02_lesson-2-applied-scenarios/

01_a-brief-introduction-into-precision-agriculture_Mulla_and_Khosla_2015.pdf

01_a-brief-introduction-into-precision-agriculture_instructions.html

02_farmer-betty-and-her-precision-agriculture-plans.en.srt

02_farmer-betty-and-her-precision-agriculture-plans.en.txt

02_farmer-betty-and-her-precision-agriculture-plans.mp4

03_what-to-consider-when-using-your-quam.en.srt

03_what-to-consider-when-using-your-quam.en.txt

03_what-to-consider-when-using-your-quam.mp4

04_farmer-betty-tried-unsupervised-learning-required_instructions.html

05_data-is-central-to-your-ml-problem-required_instructions.html

/.../03_lesson-3-getting-good-questions/

01_broad-examples-narrowed-down.en.srt

01_broad-examples-narrowed-down.en.txt

01_broad-examples-narrowed-down.mp4

02_identify-business-evaluation.en.srt

02_identify-business-evaluation.en.txt

02_identify-business-evaluation.mp4

03_everything-is-a-proxy.en.srt

03_everything-is-a-proxy.en.txt

03_everything-is-a-proxy.mp4

04_martin-zinkevichs-rules-for-ml-supplemental_instructions.html

05_weekly-summary.en.srt

05_weekly-summary.en.txt

05_weekly-summary.mp4

06_machine-learning-in-the-real-world-review_exam.html

/.../01_lesson-1-data-needs/

01_sources-of-training-data.en.srt

01_sources-of-training-data.en.txt

01_sources-of-training-data.mp4

02_how-much-data-do-i-need.en.srt

02_how-much-data-do-i-need.en.txt

02_how-much-data-do-i-need.mp4

/.../02_lesson-2-data-relates-to-problems/

01_ethical-issues.en.srt

01_ethical-issues.en.txt

01_ethical-issues.mp4

02_data-protection-laws-required_instructions.html

03_government-readings-on-data-privacy-supplemental_index.html

03_government-readings-on-data-privacy-supplemental_instructions.html

04_bias-in-data-sources.en.srt

04_bias-in-data-sources.en.txt

04_bias-in-data-sources.mp4

05_noise-and-sources-of-randomness.en.srt

05_noise-and-sources-of-randomness.en.txt

05_noise-and-sources-of-randomness.mp4

/.../03_lesson-3-data-process/

01_image-classification-example.en.srt

01_image-classification-example.en.txt

01_image-classification-example.mp4

02_data-cleaning-everybodys-favourite-task.en.srt

02_data-cleaning-everybodys-favourite-task.en.txt

02_data-cleaning-everybodys-favourite-task.mp4

03_why-you-need-to-set-up-a-data-pipeline.en.srt

03_why-you-need-to-set-up-a-data-pipeline.en.txt

03_why-you-need-to-set-up-a-data-pipeline.mp4

04_weekly-summary.en.srt

04_weekly-summary.en.txt

04_weekly-summary.mp4

05_understanding-data-for-ml_exam.html

/.../01_lesson-1-machine-learning-process-lifecycle/

01_mlpl-overview.en.srt

01_mlpl-overview.en.txt

01_mlpl-overview.mp4

02_machine-learning-process-lifecycle-explained_MLPL.pdf

02_machine-learning-process-lifecycle-explained_instructions.html

03_mlpl-as-experienced-by-farmer-betty.en.srt

03_mlpl-as-experienced-by-farmer-betty.en.txt

03_mlpl-as-experienced-by-farmer-betty.mp4

/.../02_lesson-2-getting-ready-to-model/

01_exploring-the-process-of-problem-definition.en.srt

01_exploring-the-process-of-problem-definition.en.txt

01_exploring-the-process-of-problem-definition.mp4

02_assessing-your-quam-for-use-in-your-business.en.srt

02_assessing-your-quam-for-use-in-your-business.en.txt

02_assessing-your-quam-for-use-in-your-business.mp4

/.../03_lesson-3-model-learning-and-evaluation/

01_technically-assessing-the-strength-of-your-quam.en.srt

01_technically-assessing-the-strength-of-your-quam.en.txt

01_technically-assessing-the-strength-of-your-quam.mp4

02_different-kinds-of-wrong.en.srt

02_different-kinds-of-wrong.en.txt

02_different-kinds-of-wrong.mp4

03_weekly-summary.en.srt

03_weekly-summary.en.txt

03_weekly-summary.mp4

04_deep-learning-for-identifying-metastatic-breast-cancer-advanced-supplemental_DeepLearning_BIDMC.HMS.MIT_Camelyon_2016.pdf

04_deep-learning-for-identifying-metastatic-breast-cancer-advanced-supplemental_instructions.html

05_understanding-machine-learning-projects_exam.html

/.../0. Websites you may like/

[CourserHub.com].url

/.../01_classification-in-a-nutshell/

01_math-review_Math_review.pdf

01_math-review_instructions.html

02_introduction-to-the-course.en.srt

02_introduction-to-the-course.en.txt

02_introduction-to-the-course.mp4

03_what-does-a-classifier-actually-do.en.srt

03_what-does-a-classifier-actually-do.en.txt

03_what-does-a-classifier-actually-do.mp4

04_classification-in-scikit-learn.en.srt

04_classification-in-scikit-learn.en.txt

04_classification-in-scikit-learn.mp4

/.../02_decision-trees/

01_what-are-decision-trees.en.srt

01_what-are-decision-trees.en.txt

01_what-are-decision-trees.mp4

02_generalization-and-overfitting.en.srt

02_generalization-and-overfitting.en.txt

02_generalization-and-overfitting.mp4

03_scikitlearn-documentation-for-decision-trees-optional_instructions.html

03_scikitlearn-documentation-for-decision-trees-optional_sklearn.tree.DecisionTreeClassifier.html

03_scikitlearn-documentation-for-decision-trees-optional_tree.html

04_scikitlearn-documentation-for-random-forests-optional_instructions.html

04_scikitlearn-documentation-for-random-forests-optional_sklearn.ensemble.RandomForestClassifier.html

/.../03_k-nearest-neighbours/

01_classification-using-k-nearest-neighbours.en.srt

01_classification-using-k-nearest-neighbours.en.txt

01_classification-using-k-nearest-neighbours.mp4

02_distance-measures.en.srt

02_distance-measures.en.txt

02_distance-measures.mp4

03_scikitlearn-documentation-for-k-nearest-neighbours-optional_instructions.html

03_scikitlearn-documentation-for-k-nearest-neighbours-optional_neighbors.html

04_weekly-summary.en.srt

04_weekly-summary.en.txt

04_weekly-summary.mp4

/.../01_finding-lines/

01_line-fitting.en.srt

01_line-fitting.en.txt

01_line-fitting.mp4

02_optimal-line-fitting.en.srt

02_optimal-line-fitting.en.txt

02_optimal-line-fitting.mp4

03_loss-and-convexity.en.srt

03_loss-and-convexity.en.txt

03_loss-and-convexity.mp4

04_gradient-descent.en.srt

04_gradient-descent.en.txt

04_gradient-descent.mp4

05_scikitlearn-documentation-for-linear-regression-optional_instructions.html

05_scikitlearn-documentation-for-linear-regression-optional_sklearn.linear_model.LinearRegression.html

/.../02_simple-vs-expressive/

01_nonlinear-features-and-model-complexity.en.srt

01_nonlinear-features-and-model-complexity.en.txt

01_nonlinear-features-and-model-complexity.mp4

02_bias-and-variance-tradeoff.en.srt

02_bias-and-variance-tradeoff.en.txt

02_bias-and-variance-tradeoff.mp4

03_regularizers.en.srt

03_regularizers.en.txt

03_regularizers.mp4

/.../03_from-regression-to-classification/

01_loss-for-classification.en.srt

01_loss-for-classification.en.txt

01_loss-for-classification.mp4

02_weekly-summary.en.srt

02_weekly-summary.en.txt

02_weekly-summary.mp4

/.../01_models-with-transfer-functions/

01_logistic-regression.en.srt

01_logistic-regression.en.txt

01_logistic-regression.mp4

02_neural-networks.en.srt

02_neural-networks.en.txt

02_neural-networks.mp4

/.../02_support-vector-machines/

01_hinge-loss.en.srt

01_hinge-loss.en.txt

01_hinge-loss.mp4

02_basics-of-support-vector-machines.en.srt

02_basics-of-support-vector-machines.en.txt

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/.../03_infinite-feature-expansions/

01_kernels.en.srt

01_kernels.en.txt

01_kernels.mp4

02_weekly-summary.en.srt

02_weekly-summary.en.txt

02_weekly-summary.mp4

03_scikitlearn-documentation-for-svms-optional_instructions.html

03_scikitlearn-documentation-for-svms-optional_svm.html

/.../01_model-assessment/

01_regression-assessment.en.srt

01_regression-assessment.en.txt

01_regression-assessment.mp4

02_classification-assessment.en.srt

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03_learning-curves.en.srt

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03_learning-curves.mp4

04_some-resources-on-model-assessment-optional_instructions.html

04_some-resources-on-model-assessment-optional_model_evaluation.html

/.../02_testing-and-validation-procedures/

01_testing-your-models.en.srt

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/.../03_parameter-tuning/

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02_model-parameters.en.srt

02_model-parameters.en.txt

02_model-parameters.mp4

03_weekly-summary.en.srt

03_weekly-summary.en.txt

03_weekly-summary.mp4

/.../01_svm-a-short-introduction-to-duality/

01__resources.html

/.../02_more-techniques-for-hyperparameter-tuning/

01__Gini_index_fulltext.pdf

01__bergstra12a.pdf

01__resources.html

/machine-learning-classification-algorithms/

Readme.txt

/.../01_machine-learning-strategy/01_planning/

01_introduction-to-the-course.en.srt

01_introduction-to-the-course.en.txt

01_introduction-to-the-course.mp4

02_ml-readiness.en.srt

02_ml-readiness.en.txt

02_ml-readiness.mp4

03_risk-mitigation.en.srt

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03_risk-mitigation.mp4

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04_experimental-mindset.mp4

/.../02_ownership-products/

01_ip-questions_instructions.html

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/.../03_teamwork-communication/

01_setting-up-a-team.en.srt

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02_understanding-and-communicating-change.en.srt

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02_understanding-and-communicating-change.mp4

03_weekly-summary.en.srt

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/.../01_feedback-fairness/

01_ai-4-good-for-all.en.srt

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01_ai-4-good-for-all.mp4

02_positive-feedback-loops-negative-feedback-loops.en.srt

02_positive-feedback-loops-negative-feedback-loops.en.txt

02_positive-feedback-loops-negative-feedback-loops.mp4

/.../02_you-are-what-you-optimize-design-considerations/

01_metric-design-observing-behaviours.en.srt

01_metric-design-observing-behaviours.en.txt

01_metric-design-observing-behaviours.mp4

02_secondary-effects-of-optimization.en.srt

02_secondary-effects-of-optimization.en.txt

02_secondary-effects-of-optimization.mp4

/.../03_legalities-and-best-practices/

01_regulatory-concerns.en.srt

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01_regulatory-concerns.mp4

02_weekly-summary.en.srt

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/.../01_design-considerations/

01_integrating-info-systems.en.srt

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01_integrating-info-systems.mp4

02_users-break-things.en.srt

02_users-break-things.en.txt

02_users-break-things.mp4

03_time-space-complexity-in-production.en.srt

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03_time-space-complexity-in-production.mp4

/.../02_deployment-issues-operational-processes/

01_when-do-i-retrain-the-model.en.srt

01_when-do-i-retrain-the-model.en.txt

01_when-do-i-retrain-the-model.mp4

02_logging-ml-model-versioning.en.srt

02_logging-ml-model-versioning.en.txt

02_logging-ml-model-versioning.mp4

/.../03_communicating-technical-content/

01_knowledge-transfer.en.srt

01_knowledge-transfer.en.txt

01_knowledge-transfer.mp4

02_reporting-performance-to-stakeholders.en.srt

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02_reporting-performance-to-stakeholders.mp4

03_weekly-summary.en.srt

03_weekly-summary.en.txt

03_weekly-summary.mp4

/.../01_mapping-the-model-lifecycle/

01_mlpl-recap.en.srt

01_mlpl-recap.en.txt

01_mlpl-recap.mp4

02_post-deployment-challenges.en.srt

02_post-deployment-challenges.en.txt

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/.../02_maintenance-checkpoints/

01_quam-monitoring-and-logging.en.srt

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01_quam-monitoring-and-logging.mp4

02_quam-testing.en.srt

02_quam-testing.en.txt

02_quam-testing.mp4

03_quam-maintenance.en.srt

03_quam-maintenance.en.txt

03_quam-maintenance.mp4

04_quam-updating.en.srt

04_quam-updating.en.txt

04_quam-updating.mp4

/.../03_scaling-up/

01_separating-datastack-from-production.en.srt

01_separating-datastack-from-production.en.txt

01_separating-datastack-from-production.mp4

02_dashboard-essentials-metrics-monitoring.en.srt

02_dashboard-essentials-metrics-monitoring.en.txt

02_dashboard-essentials-metrics-monitoring.mp4

03_weekly-summary.en.srt

03_weekly-summary.en.txt

03_weekly-summary.mp4

Total files 465