Statistical Learning for Data Science Specialization

About Course

Master Statistical Learning for Data Science: A Free Coursera Specialization

Unlock the power of statistical learning and become a data science expert with this comprehensive specialization from the University of Colorado. This program equips you with advanced techniques for model selection, including regression, classification, trees, SVM, unsupervised learning, splines, and resampling methods. You’ll gain a deep understanding of coefficient estimation and interpretation, essential for explaining and justifying your models to clients and companies.

Key Benefits:

Free Access: This specialization is completely free, providing an affordable path to data science mastery.
Expert Instruction: Learn from leading university and industry experts in statistical learning.
Hands-On Learning: Master concepts through interactive programming assignments and applied projects.
In-Depth Knowledge: Develop a deep understanding of key statistical learning principles.
Career Advancement: Enhance your skills and gain a competitive edge in the data science field.

Course Content Includes:

Regression Analysis
Classification Methods
Decision Trees and Ensemble Methods
Support Vector Machines (SVM)
Unsupervised Learning Techniques
Splines and Resampling Methods
Coefficient Estimation and Interpretation

This specialization is part of the University of Colorado’s Master of Data Science (MS-DS) degree program offered on Coursera. Earn academic credit and boost your career prospects.

Start your free journey to data science expertise today!

Note: This course is offered by Coursera, but it is available for free on Theetay.

Course Content

01. Regression and Classification

A Message from the Professor
001 01_earn-academic-credit-for-your-work_instructions.html

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002 02_course-support_instructions.html

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005 01_introduction-and-welcome.mp4

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008 02_supervised-vs-unsupervised.mp4

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011 03_notation-overview.mp4

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014 04_overview-example-discussion.mp4

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017 01_prediction.mp4

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020 02_inference.mp4

00:00
023 03_parametric-methods.mp4

00:00
026 04_interpretability-vs-flexibility.mp4

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029 05_quantitative-vs-qualitative.mp4

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032 01_model-accuracy.mp4

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035 02_bias-variance-trade-off.mp4

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038 03_assessing-accuracy-classification.mp4

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041 01_bayes-classifier-part-i.mp4

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044 02_bayes-classifier-part-ii.mp4

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047 03_assessing-accuracy-knn.mp4

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050 01_simple-linear-regression-overview.mp4

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053 02_coefficient-estimation.mp4

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056 03_accuracy-of-coefficient-estimates.mp4

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059 01_model-accuracy.mp4

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062 02_correlation.mp4

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065 01_multiple-linear-regression-overview.mp4

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068 02_relationship-between-x-and-y.mp4

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071 01_qualitative-predictors.mp4

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074 02_interaction-terms.mp4

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077 01_multicollinearity.mp4

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080 02_linear-regression-vs-knn-regression.mp4

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083 01_classification-overview.mp4

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086 02_linear-vs-logistics-regression.mp4

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089 03_logistic-regression.mp4

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092 01_estimating-coefficients.mp4

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095 02_multiple-logistic-regression.mp4

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098 01_generative-models-part-i.mp4

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101 02_generative-models-part-ii.mp4

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103 01_lda.en.txt

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106 02_lda-estimates.mp4

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109 03_lda-with-p-1.mp4

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112 04_standard-to-multivariate-details.mp4

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115 01_qda.mp4

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118 02_naive-bayes.mp4

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121 01_poisson-regression.mp4

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124 02_link-functions-and-conclusion.mp4

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Section Quiz

02. Resampling, Selection and Splines

001 01_earn-academic-credit-for-your-work_instructions.html

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002 02_course-support_instructions.html

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005 03_course-introduction.mp4

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008 01_generalized-linear-models-part-1.mp4

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011 02_generalized-linear-models-part-2.mp4

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014 01_parametric-vs-non-parametric-regression.mp4

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020 02_general-additive-models-part-2.mp4

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023 01_generalized-least-squares.mp4

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024 02_generalized-least-squares-gls-relations-to-ols-wls_instructions.html

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027 01_l1-and-l2-norms.mp4

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030 02_ridge-regression-part-1.mp4

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033 03_ridge-regression-part-2.mp4

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036 04_ridge-regression-part-3.mp4

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037 05_ridge-regression_instructions.html

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040 01_lasso.mp4

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041 02_lasso_instructions.html

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044 01_principle-component-analysis-pca-overview.mp4

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047 02_pca-in-terms-of-svd.mp4

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048 03_principle-component-analysis_instructions.html

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051 01_cross-validation.mp4

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052 02_summary_instructions.html

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056 01_bootstrapping.mp4

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057 02_summary_instructions.html

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Section Quiz

03. Trees, SVM and Unsupervised Learning

003 01_course-3-introduction.mp4

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004 02_earn-academic-credit-for-your-work_instructions.html

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005 03_course-support_instructions.html

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008 01_support-vector-machines-part-1.mp4

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011 02_support-vector-machines-part-2.mp4

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014 03_support-vector-machines-part-3.mp4

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017 04_support-vector-machines-part-4.mp4

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018 05_support-vector-machines_instructions.html

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021 01_neural-networks-part-1.mp4

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024 02_neural-networks-part-2.mp4

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027 03_neural-networks-part-3.mp4

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030 04_neural-networks-part-4.mp4

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033 05_neural-networks-and-its-application-to-unsupervised-learning.mp4

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035 06_neural-networks_instructions.html

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038 01_decision-trees.mp4

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039 02_decision-trees-and-bagging_instructions.html

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links.txt

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Section Quiz

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About Course

**Master Statistical Learning for Data Science: A Free Coursera Specialization**

What Will You Learn?

Course Content

01. Regression and Classification

A Message from the Professor

001 01_earn-academic-credit-for-your-work_instructions.html

002 02_course-support_instructions.html

005 01_introduction-and-welcome.mp4

008 02_supervised-vs-unsupervised.mp4

011 03_notation-overview.mp4

014 04_overview-example-discussion.mp4

017 01_prediction.mp4

020 02_inference.mp4

023 03_parametric-methods.mp4

026 04_interpretability-vs-flexibility.mp4

029 05_quantitative-vs-qualitative.mp4

032 01_model-accuracy.mp4

035 02_bias-variance-trade-off.mp4

038 03_assessing-accuracy-classification.mp4

041 01_bayes-classifier-part-i.mp4

044 02_bayes-classifier-part-ii.mp4

047 03_assessing-accuracy-knn.mp4

050 01_simple-linear-regression-overview.mp4

053 02_coefficient-estimation.mp4

056 03_accuracy-of-coefficient-estimates.mp4

059 01_model-accuracy.mp4

062 02_correlation.mp4

065 01_multiple-linear-regression-overview.mp4

068 02_relationship-between-x-and-y.mp4

071 01_qualitative-predictors.mp4

074 02_interaction-terms.mp4

077 01_multicollinearity.mp4

080 02_linear-regression-vs-knn-regression.mp4

083 01_classification-overview.mp4

086 02_linear-vs-logistics-regression.mp4

089 03_logistic-regression.mp4

092 01_estimating-coefficients.mp4

095 02_multiple-logistic-regression.mp4

098 01_generative-models-part-i.mp4

101 02_generative-models-part-ii.mp4

103 01_lda.en.txt

106 02_lda-estimates.mp4

109 03_lda-with-p-1.mp4

112 04_standard-to-multivariate-details.mp4

115 01_qda.mp4

118 02_naive-bayes.mp4

121 01_poisson-regression.mp4

124 02_link-functions-and-conclusion.mp4

Section Quiz

02. Resampling, Selection and Splines

001 01_earn-academic-credit-for-your-work_instructions.html

002 02_course-support_instructions.html

005 03_course-introduction.mp4

008 01_generalized-linear-models-part-1.mp4

011 02_generalized-linear-models-part-2.mp4

014 01_parametric-vs-non-parametric-regression.mp4

020 02_general-additive-models-part-2.mp4

023 01_generalized-least-squares.mp4

024 02_generalized-least-squares-gls-relations-to-ols-wls_instructions.html

027 01_l1-and-l2-norms.mp4

030 02_ridge-regression-part-1.mp4

033 03_ridge-regression-part-2.mp4

036 04_ridge-regression-part-3.mp4

037 05_ridge-regression_instructions.html

040 01_lasso.mp4

041 02_lasso_instructions.html

044 01_principle-component-analysis-pca-overview.mp4

047 02_pca-in-terms-of-svd.mp4

048 03_principle-component-analysis_instructions.html

051 01_cross-validation.mp4

052 02_summary_instructions.html

056 01_bootstrapping.mp4

057 02_summary_instructions.html

Section Quiz

03. Trees, SVM and Unsupervised Learning

003 01_course-3-introduction.mp4

004 02_earn-academic-credit-for-your-work_instructions.html

005 03_course-support_instructions.html

008 01_support-vector-machines-part-1.mp4

Master Statistical Learning for Data Science: A Free Coursera Specialization