About Course

IBM Introduction to Machine Learning Specialization – FREE!

Want to unlock the power of machine learning and land a high-paying job? This **free** IBM Introduction to Machine Learning Specialization course from Coursera will equip you with the essential skills needed to succeed in the data science and machine learning fields. Learn from IBM experts and gain a comprehensive understanding of machine learning algorithms and artificial intelligence, including:

Understanding machine learning applications in various business scenarios
Predicting future outcomes and explaining behaviors
Evaluating machine learning models and applying best practices
Building a strong portfolio of machine learning projects

Upon completion, you’ll receive a certificate from Coursera and an IBM Badge of Honor, showcasing your skills to potential employers. This course is part of the 6-part IBM Machine Learning Professional Certificate series, offering a complete pathway into a rewarding career in machine learning.

**Enroll in this FREE course today and start your journey towards a successful career in machine learning!**

**This course is available on Theetay.com, a platform offering free access to top-rated courses from leading providers like Udemy, Udacity, Coursera, MasterClass, NearPeer and more. **

Course Content

01. ibm-exploratory-data-analysis-for-machine-learning
new topic

0057 003_handling-missing-values-and-outliers-using-residuals.mp4

00:00
0061 001_introduction-to-exploratory-data-analysis-eda.mp4

00:00
0064 002_eda-with-visualization.mp4

00:00
0067 003_grouping-data-for-eda.mp4

00:00
0068 004_optional-download-assets-for-lab-exploratory-data-analysis-lab_01c_LAB_EDA.zip

00:00
0072 005_optional-solution-eda-notebook-part-1.mp4

00:00
0075 006_optional-solution-eda-notebook-part-2.mp4

00:00
0078 007_optional-solution-eda-notebook-part-3.mp4

00:00
0081 008_optional-solution-eda-notebook-part-4.mp4

00:00
0084 001_feature-engineering-and-variable-transformation-background.mp4

00:00
0087 002_variable-transformation.mp4

00:00
0090 003_feature-encoding.mp4

00:00
0093 004_feature-scaling.mp4

00:00
0096 005_common-variable-transformations-in-python.mp4

00:00
0097 006_optional-download-assets-for-lab-feature-engineering-demo_01d_DEMO_Feature_Engineering.zip

00:00
0101 007_optional-solution-feature-engineering-lab-part-1.mp4

00:00
0104 008_optional-solution-feature-engineering-lab-part-2.mp4

00:00
0107 009_optional-solution-feature-engineering-lab-part-3.mp4

00:00
0111 001_estimation-and-inference-introduction.mp4

00:00
0114 002_estimation-and-inference-example.mp4

00:00
0117 003_estimation-and-inference-parametric-vs-non-parametric.mp4

00:00
0120 004_estimation-and-inference-commonly-used-distributions.mp4

00:00
0123 005_frequentist-vs-bayesian-statistics.mp4

00:00
0126 001_introduction-to-hypothesis.mp4

00:00
0129 002_hypothesis-testing-example.mp4

00:00
0132 003_bayesian-interpretation-of-hypothesis-testing-example.mp4

00:00
0135 004_type-1-vs-type-2-error.mp4

00:00
0138 005_type-1-vs-type-2-error-examples.mp4

00:00
0141 006_hypothesis-testing-terminology.mp4

00:00
0144 007_significance-level-and-p-values.mp4

00:00
0147 008_significance-level-and-p-values-and-the-f-statistic.mp4

00:00
0148 009_optional-download-assets-for-lab-hypothesis-testing-demo_01e_DEMO_Hypothesis_Testing.zip

00:00
0152 010_optional-hypothesis-testing-demo-part-1.mp4

00:00
0155 011_optional-hypothesis-testing-demo-part-2.mp4

00:00
0158 012_correlation-vs-causation.mp4

00:00
0159 001_summary-review_instructions.html

00:00
0004 002_course-prerequisites_instructions.html

00:00
0003 001_course-introduction.mp4

00:00
0010 002_machine-learning-and-deep-learning.mp4

00:00
0013 003_machine-learning-and-deep-learning-part-1.mp4

00:00
0016 004_machine-learning-and-deep-learning-part-2.mp4

00:00
0019 005_history-of-ai.mp4

00:00
0022 006_history-of-machine-learning-and-deep-learning.mp4

00:00
0025 001_modern-ai.mp4

00:00
0028 002_applications.mp4

00:00
0031 003_machine-learning-workflow.mp4

00:00
0035 001_retrieving-data-from-csv-and-json-files.mp4

00:00
0038 002_retrieving-data-from-databases-apis-and-the-cloud.mp4

00:00
0039 003_optional-download-assets-for-lab-reading-data-in-database-files-part-a_01a_DEMO_Reading_Data.zip

00:00
0043 004_optional-lab-solution-reading-data-jupyter-notebook-part-a.mp4

00:00
0044 005_optional-download-assets-for-lab-reading-data-in-jupyter-notebook-part-b_01b_LAB_Reading_Data.zip

00:00
0048 006_optional-lab-solution-reading-in-database-files-part-b.mp4

00:00
0051 001_data-cleaning.mp4

00:00
0054 002_handling-missing-values-and-outliers.mp4

00:00
Section Quiz

02. supervised-machine-learning-regression
new topic

0050 005_optional-training-and-test-splits-lab-part-3.mp4

00:00
A Message from the Professor
0053 006_optional-training-and-test-splits-lab-part-4.mp4

00:00
0056 001_polynomial-regression.mp4

00:00
0063 002_cross-validation-demo-part-1.mp4

00:00
0066 003_cross-validation-demo-part-2.mp4

00:00
0069 004_cross-validation-demo-part-3.mp4

00:00
0072 005_cross-validation-demo-part-4.mp4

00:00
0075 006_cross-validation-demo-part-5.mp4

00:00
0079 001_bias-variance-trade-off-part-1.mp4

00:00
0082 002_bias-variance-trade-off-part-2.mp4

00:00
0085 003_regularization-and-model-selection.mp4

00:00
0088 004_ridge-regression.mp4

00:00
0091 005_lasso-regression-part-1.mp4

00:00
0094 006_lasso-regression-part-2.mp4

00:00
0097 007_elastic-net.mp4

00:00
0100 008_recursive-feature-elimination.mp4

00:00
0103 001_polynomial-features-and-regularization-demo-part-1.mp4

00:00
0106 002_polynomial-features-and-regularization-demo-part-2.mp4

00:00
0109 003_polynomial-features-and-regularization-demo-part-3.mp4

00:00
0113 001_further-details-of-regularization-part-1.mp4

00:00
0116 002_further-details-of-regularization-part-2.mp4

00:00
0119 003_optional-details-of-regularization-part-1.mp4

00:00
0122 004_optional-details-of-regularization-part-2.mp4

00:00
0125 005_optional-details-of-regularization-part-3.mp4

00:00
0126 001_summary-review_instructions.html

00:00
0004 002_course-prerequisites_instructions.html

00:00
0007 001_introduction-to-supervised-machine-learning-types-of-machine-learning-part-1.mp4

00:00
0010 002_introduction-to-supervised-machine-learning-types-of-machine-learning-part-2.mp4

00:00
0013 003_supervised-machine-learning-part-1.mp4

00:00
0016 004_supervised-machine-learning-part-2.mp4

00:00
0019 005_regression-and-classification-examples.mp4

00:00
0022 001_introduction-to-linear-regression-part-1.mp4

00:00
0025 002_introduction-to-linear-regression-part-2.mp4

00:00
0028 003_optional-linear-regression-demo-part1.mp4

00:00
0031 004_optional-linear-regression-demo-part2.mp4

00:00
0034 005_optional-linear-regression-demo-part3.mp4

00:00
0038 001_training-and-test-splits-part-1.mp4

00:00
0041 002_training-and-test-splits-part-2.mp4

00:00
0044 003_optional-training-and-test-splits-lab-part-1.mp4

00:00
0047 004_optional-training-and-test-splits-lab-part-2.mp4

00:00
Section Quiz

03. supervised-machine-learning-classification
new topic

0004 002_welcome.mp4

00:00
0005 003_optional-download-data-assets_Data_and_Python_Assets.zip

00:00
0009 001_introduction-what-is-classification.mp4

00:00
0012 002_introduction-to-logistic-regression.mp4

00:00
0015 003_classification-with-logistic-regression.mp4

00:00
0018 004_logistic-regression-with-multi-classes.mp4

00:00
0021 005_implementing-logistic-regression-models.mp4

00:00
0024 006_confusion-matrix-accuracy-specificity-precision-and-recall.mp4

00:00
0027 007_classification-error-metrics-roc-and-precision-recall-curves.mp4

00:00
0030 008_implementing-the-calculation-of-roc-and-precision-recall-curves.mp4

00:00
0031 001_optional-download-assets-for-demo-lab-logistic-regression_Demo_Lab-Logistic_Regression_Assets.zip

00:00
0035 002_optional-logistic-regression-lab-part-1.mp4

00:00
0038 003_optional-logistic-regression-lab-part-2.mp4

00:00
0041 004_optional-logistic-regression-lab-part-3.mp4

00:00
0045 001_k-nearest-neighbors-for-classification.mp4

00:00
0048 002_k-nearest-neighbors-decision-boundary.mp4

00:00
0051 003_k-nearest-neighbors-distance-measurement.mp4

00:00
0054 004_k-nearest-neighbors-pros-and-cons.mp4

00:00
0057 005_k-nearest-neighbors-with-feature-scaling.mp4

00:00
0060 001_optional-k-nearest-neighbors-notebook-part-1.mp4

00:00
0063 002_optional-k-nearest-neighbors-notebook-part-2.mp4

00:00
0066 003_optional-k-nearest-neighbors-notebook-part-3.mp4

00:00
0070 001_introduction-to-support-vector-machines.mp4

00:00
0073 002_classification-with-support-vector-machines.mp4

00:00
0076 003_the-support-vector-machines-cost-function.mp4

00:00
0079 004_regularization-in-support-vector-machines.mp4

00:00
0082 001_introduction-to-support-vector-machines-gaussian-kernels.mp4

00:00
0085 002_support-vector-machines-gaussian-kernels-part-1.mp4

00:00
0088 003_support-vector-machines-gaussian-kernels-part-2.mp4

00:00
0091 004_support-vector-machines-workflow.mp4

00:00
0094 005_implementing-support-vector-machines-kernal-models.mp4

00:00
0097 001_optional-support-vector-machines-notebook-part-1.mp4

00:00
0100 002_optional-support-vector-machines-notebook-part-2.mp4

00:00
0103 003_optional-support-vector-machines-notebook-part-3.mp4

00:00
0107 001_overview-of-classifiers.mp4

00:00
0110 002_introduction-to-decision-trees.mp4

00:00
0113 003_building-a-decision-tree.mp4

00:00
0116 004_entropy-based-splitting.mp4

00:00
0119 005_other-decision-tree-splitting-criteria.mp4

00:00
0122 006_pros-and-cons-of-decision-trees.mp4

00:00
0123 001_optional-download-assets-for-demo-lab-decision-trees_Demo_Lab-Decision_Trees_Assets.zip

00:00
0127 002_optional-decision-trees-notebook-part-1.mp4

00:00
0001 001_about-this-course_instructions.html

00:00
0130 003_optional-decision-trees-notebook-part-2.mp4

00:00
0133 004_optional-decision-trees-notebook-part-3.mp4

00:00
0137 001_ensemble-based-methods-and-bagging-part-1.mp4

00:00
0140 002_ensemble-based-methods-and-bagging-part-2.mp4

00:00
0143 003_ensemble-based-methods-and-bagging-part-3.mp4

00:00
0146 001_random-forest.mp4

00:00
0151 002_optional-bagging-notebook-part-1.mp4

00:00
0154 003_optional-bagging-notebook-part-2.mp4

00:00
0157 004_optional-bagging-notebook-part-3.mp4

00:00
0160 001_review-of-bagging.mp4

00:00
0163 002_overview-of-boosting.mp4

00:00
0166 003_adaboost-and-gradient-boosting-overview.mp4

00:00
0169 004_adaboost-and-gradient-boosting-syntax.mp4

00:00
0172 005_stacking.mp4

00:00
0173 001_optional-download-assets-for-demo-lab-boosting-and-stacking_Demo_Lab-Boosting_and_Stacking_Assets.zip

00:00
0177 002_optional-boosting-notebook-part-1.mp4

00:00
0180 003_optional-boosting-notebook-part-2.mp4

00:00
0183 004_optional-boosting-notebook-part-3.mp4

00:00
0187 001_model-interpretability.mp4

00:00
0190 002_examples-of-self-interpretable-and-non-self-interpretable-models.mp4

00:00
0193 003_model-agnostic-explanations.mp4

00:00
0196 004_surrogate-models.mp4

00:00
0199 001_introduction-to-unbalanced-classes.mp4

00:00
0202 002_upsampling-and-downsampling.mp4

00:00
0205 003_modeling-approaches-weighting-and-stratified-sampling.mp4

00:00
0208 004_modeling-approaches-random-and-synthetic-oversampling.mp4

00:00
0211 005_modeling-approaches-nearing-neighbor-methods.mp4

00:00
0214 006_modeling-approaches-blagging.mp4

00:00
0215 001_summary-review_instructions.html

00:00

04. ibm-unsupervised-machine-learning
new topic

0006 002_introduction-to-unsupervised-learning-overview.mp4

00:00
0009 003_introduction-to-unsupervised-learning-use-cases-of-clustering.mp4

00:00
0012 004_introduction-to-clustering.mp4

00:00
0015 001_k-means.mp4

00:00
0018 002_k-means-initialization.mp4

00:00
0021 003_selecting-the-right-number-of-clusters-in-k-means.mp4

00:00
0024 004_elbow-method-and-applying-k-means.mp4

00:00
0027 005_optional-k-means-notebook-part-1.mp4

00:00
0030 006_k-means-notebook-part-2.mp4

00:00
0033 007_optional-k-means-notebook-part-3.mp4

00:00
0034 008_mixture-of-gaussians_GMM_reading.html

00:00
0039 001_distance-metrics-euclidean-and-manhattan-distance.mp4

00:00
0042 002_distance-metrics-cosine-and-jaccard-distance.mp4

00:00
0045 003_curse-of-dimensionality-notebook-part-1.mp4

00:00
0048 004_curse-of-dimensionality-notebook-part-2.mp4

00:00
0051 005_curse-of-dimensionality-notebook-part-3.mp4

00:00
0054 006_curse-of-dimensionality-notebook-part-4.mp4

00:00
0058 001_hierarchical-agglomerative-clustering.mp4

00:00
0061 002_hierarchical-agglomerative-clustering-hierarchical-linkage-types.mp4

00:00
0064 003_applying-hierarchical-agglomerative-clustering.mp4

00:00
0067 004_dbscan.mp4

00:00
0070 005_visualizing-dbscan.mp4

00:00
0073 006_mean-shift.mp4

00:00
0076 001_comparing-algorithms.mp4

00:00
0079 002_clustering-notebook-part-1.mp4

00:00
0082 003_clustering-notebook-part-2.mp4

00:00
0085 004_optional-clustering-notebook-part-3.mp4

00:00
0088 005_clustering-notebook-part-4.mp4

00:00
0092 001_dimensionality-reduction-overview.mp4

00:00
0095 002_dimensionality-reduction-principal-component-analysis.mp4

00:00
0098 003_optional-dimensionality-reduction-notebook-part-1.mp4

00:00
0101 004_dimensionality-reduction-notebook-part-2.mp4

00:00
0104 005_dimensionality-reduction-imaging-example.mp4

00:00
0108 001_kernel-principal-component-analysis-and-multidimensional-scaling.mp4

00:00
0111 002_dimensionality-reduction-notebook-part-3.mp4

00:00
0115 001_non-negative-matrix-factorization.mp4

00:00
0118 002_non-negative-matrix-factorization-notebook-part-1.mp4

00:00
0121 003_non-negative-matrix-factorization-notebook-part-2.mp4

00:00
0122 001_summary_instructions.html

00:00
Section Quiz

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About Course

IBM Introduction to Machine Learning Specialization – FREE!

What Will You Learn?

Course Content

01. ibm-exploratory-data-analysis-for-machine-learning new topic

0057 003_handling-missing-values-and-outliers-using-residuals.mp4

0061 001_introduction-to-exploratory-data-analysis-eda.mp4

0064 002_eda-with-visualization.mp4

0067 003_grouping-data-for-eda.mp4

0068 004_optional-download-assets-for-lab-exploratory-data-analysis-lab_01c_LAB_EDA.zip

0072 005_optional-solution-eda-notebook-part-1.mp4

0075 006_optional-solution-eda-notebook-part-2.mp4

0078 007_optional-solution-eda-notebook-part-3.mp4

0081 008_optional-solution-eda-notebook-part-4.mp4

0084 001_feature-engineering-and-variable-transformation-background.mp4

0087 002_variable-transformation.mp4

0090 003_feature-encoding.mp4

0093 004_feature-scaling.mp4

0096 005_common-variable-transformations-in-python.mp4

0097 006_optional-download-assets-for-lab-feature-engineering-demo_01d_DEMO_Feature_Engineering.zip

0101 007_optional-solution-feature-engineering-lab-part-1.mp4

0104 008_optional-solution-feature-engineering-lab-part-2.mp4

0107 009_optional-solution-feature-engineering-lab-part-3.mp4

0111 001_estimation-and-inference-introduction.mp4

0114 002_estimation-and-inference-example.mp4

0117 003_estimation-and-inference-parametric-vs-non-parametric.mp4

0120 004_estimation-and-inference-commonly-used-distributions.mp4

0123 005_frequentist-vs-bayesian-statistics.mp4

0126 001_introduction-to-hypothesis.mp4

0129 002_hypothesis-testing-example.mp4

0132 003_bayesian-interpretation-of-hypothesis-testing-example.mp4

0135 004_type-1-vs-type-2-error.mp4

0138 005_type-1-vs-type-2-error-examples.mp4

0141 006_hypothesis-testing-terminology.mp4

0144 007_significance-level-and-p-values.mp4

0147 008_significance-level-and-p-values-and-the-f-statistic.mp4

0148 009_optional-download-assets-for-lab-hypothesis-testing-demo_01e_DEMO_Hypothesis_Testing.zip

0152 010_optional-hypothesis-testing-demo-part-1.mp4

0155 011_optional-hypothesis-testing-demo-part-2.mp4

0158 012_correlation-vs-causation.mp4

0159 001_summary-review_instructions.html

0004 002_course-prerequisites_instructions.html

0003 001_course-introduction.mp4

0010 002_machine-learning-and-deep-learning.mp4

0013 003_machine-learning-and-deep-learning-part-1.mp4

0016 004_machine-learning-and-deep-learning-part-2.mp4

0019 005_history-of-ai.mp4

0022 006_history-of-machine-learning-and-deep-learning.mp4

0025 001_modern-ai.mp4

0028 002_applications.mp4

0031 003_machine-learning-workflow.mp4

0035 001_retrieving-data-from-csv-and-json-files.mp4

0038 002_retrieving-data-from-databases-apis-and-the-cloud.mp4

0039 003_optional-download-assets-for-lab-reading-data-in-database-files-part-a_01a_DEMO_Reading_Data.zip

0043 004_optional-lab-solution-reading-data-jupyter-notebook-part-a.mp4

0044 005_optional-download-assets-for-lab-reading-data-in-jupyter-notebook-part-b_01b_LAB_Reading_Data.zip

0048 006_optional-lab-solution-reading-in-database-files-part-b.mp4

0051 001_data-cleaning.mp4

0054 002_handling-missing-values-and-outliers.mp4

Section Quiz

02. supervised-machine-learning-regression new topic

0050 005_optional-training-and-test-splits-lab-part-3.mp4

A Message from the Professor

0053 006_optional-training-and-test-splits-lab-part-4.mp4

0056 001_polynomial-regression.mp4

0063 002_cross-validation-demo-part-1.mp4

0066 003_cross-validation-demo-part-2.mp4

0069 004_cross-validation-demo-part-3.mp4

0072 005_cross-validation-demo-part-4.mp4

0075 006_cross-validation-demo-part-5.mp4

0079 001_bias-variance-trade-off-part-1.mp4

0082 002_bias-variance-trade-off-part-2.mp4

0085 003_regularization-and-model-selection.mp4

0088 004_ridge-regression.mp4

0091 005_lasso-regression-part-1.mp4

0094 006_lasso-regression-part-2.mp4

0097 007_elastic-net.mp4

0100 008_recursive-feature-elimination.mp4

0103 001_polynomial-features-and-regularization-demo-part-1.mp4

0106 002_polynomial-features-and-regularization-demo-part-2.mp4

01. ibm-exploratory-data-analysis-for-machine-learning
new topic

02. supervised-machine-learning-regression
new topic

03. supervised-machine-learning-classification
new topic