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[FreeCoursesOnline Me] Coursera – Applied Machine Learning in Python | Tutorial | MP4,URL | 881.09 MiB
395 kb/s 1280×720 | AAC 128 kb/s 1 CH eng
189M 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn 32M 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.mp4 20K 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.srt 45M 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.mp4 20K 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.srt 13M 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.mp4 8.0K 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.srt 32M 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.mp4 16K 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.srt 33M 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.mp4 16K 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.srt 37M 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.mp4 28K 001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.srt 317M 002.Module 2 Supervised Machine Learning 38M 002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.mp4 24K 002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.srt 20M 002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.mp4 16K 002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.srt 12M 002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.mp4 8.0K 002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.srt 23M 002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.mp4 20K 002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.srt 31M 002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.mp4 24K 002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.srt 40M 002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4 28K 002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.srt 21M 002.Module 2 Supervised Machine Learning/013. Logistic Regression.mp4 20K 002.Module 2 Supervised Machine Learning/013. Logistic Regression.srt 23M 002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.mp4 16K 002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.srt 16M 002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.mp4 12K 002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.srt 40M 002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.mp4 28K 002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.srt 20M 002.Module 2 Supervised Machine Learning/017. Cross-Validation.mp4 16K 002.Module 2 Supervised Machine Learning/017. Cross-Validation.srt 38M 002.Module 2 Supervised Machine Learning/018. Decision Trees.mp4 32K 002.Module 2 Supervised Machine Learning/018. Decision Trees.srt 161M 003.Module 3 Evaluation 47M 003.Module 3 Evaluation/019. Model Evaluation & Selection.mp4 32K 003.Module 3 Evaluation/019. Model Evaluation & Selection.srt 21M 003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.mp4 16K 003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.srt 13M 003.Module 3 Evaluation/021. Classifier Decision Functions.mp4 12K 003.Module 3 Evaluation/021. Classifier Decision Functions.srt 9.3M 003.Module 3 Evaluation/022. Precision-recall and ROC curves.mp4 8.0K 003.Module 3 Evaluation/022. Precision-recall and ROC curves.srt 20M 003.Module 3 Evaluation/023. Multi-Class Evaluation.mp4 16K 003.Module 3 Evaluation/023. Multi-Class Evaluation.srt 18M 003.Module 3 Evaluation/024. Regression Evaluation.mp4 8.0K 003.Module 3 Evaluation/024. Regression Evaluation.srt 35M 003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4 20K 003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.srt 152M 004.Module 4 Supervised Machine Learning - Part 2 22M 004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.mp4 12K 004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.srt 27M 004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.mp4 20K 004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.srt 12M 004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.mp4 12K 004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.srt 42M 004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.mp4 28K 004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.srt 18M 004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).mp4 12K 004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).srt 33M 004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.mp4 20K 004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.srt 54M 005.Optional Unsupervised Machine Learning 11M 005.Optional Unsupervised Machine Learning/032. Introduction.mp4 8.0K 005.Optional Unsupervised Machine Learning/032. Introduction.srt 17M 005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.mp4 16K 005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.srt 28M 005.Optional Unsupervised Machine Learning/034. Clustering.mp4 20K 005.Optional Unsupervised Machine Learning/034. Clustering.srt 9.9M 006.Conclusion 9.9M 006.Conclusion/035. Conclusion.mp4 4.0K 006.Conclusion/035. Conclusion.srt 4.0K [FTU Forum].url 4.0K [FreeCoursesOnline.Me].url 4.0K [FreeTutorials.Us].url 882M total
File: 001. Introduction.mp4 Size: 32558618 bytes (31.05 MiB), duration: 00:11:00, avg.bitrate: 395 kb/s Audio: aac, 44100 Hz, mono (eng) Video: h264, yuv420p, 1280x720, 29.97 fps(r) (und)
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