| 001. Introduction.mp4 | 31.1 MB | ||
| 001. Introduction.srt | 16.1 KB | ||
| 002. Key Concepts in Machine Learning.mp4 | 44.6 MB | ||
| 002. Key Concepts in Machine Learning.srt | 18.8 KB | ||
| 003. Python Tools for Machine Learning.mp4 | 12.9 MB | ||
| 003. Python Tools for Machine Learning.srt | 6.1 KB | ||
| 004. An Example Machine Learning Problem.mp4 | 31.7 MB | ||
| 004. An Example Machine Learning Problem.srt | 14.8 KB | ||
| 005. Examining the Data.mp4 | 32.2 MB | ||
| 005. Examining the Data.srt | 12.1 KB | ||
| 006. K-Nearest Neighbors Classification.mp4 | 36.2 MB | ||
| 006. K-Nearest Neighbors Classification.srt | 26.2 KB | ||
| 007. Introduction to Supervised Machine Learning.mp4 | 37.9 MB | ||
| 007. Introduction to Supervised Machine Learning.srt | 22.1 KB | ||
| 008. Overfitting and Underfitting.mp4 | 19.5 MB | ||
| 008. Overfitting and Underfitting.srt | 15.8 KB | ||
| 009. Supervised Learning Datasets.mp4 | 11.2 MB | ||
| 009. Supervised Learning Datasets.srt | 6.7 KB | ||
| 010. K-Nearest Neighbors Classification and Regression.mp4 | 22.5 MB | ||
| 010. K-Nearest Neighbors Classification and Regression.srt | 17.1 KB | ||
| 011. Linear Regression Least-Squares.mp4 | 30.1 MB | ||
| 011. Linear Regression Least-Squares.srt | 21.3 KB | ||
| 012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4 | 39.9 MB | ||
| 012. Linear Regression Ridge, Lasso, and Polynomial Regression.srt | 27.2 KB | ||
| 013. Logistic Regression.mp4 | 20.3 MB | ||
| 013. Logistic Regression.srt | 17.1 KB | ||
| 014. Linear Classifiers Support Vector Machines.mp4 | 22.7 MB | ||
| 014. Linear Classifiers Support Vector Machines.srt | 15.5 KB | ||
| 015. Multi-Class Classification.mp4 | 15.4 MB | ||
| 015. Multi-Class Classification.srt | 8.3 KB | ||
| 016. Kernelized Support Vector Machines.mp4 | 39.1 MB | ||
| 016. Kernelized Support Vector Machines.srt | 25.6 KB | ||
| 017. Cross-Validation.mp4 | 20 MB | ||
| 017. Cross-Validation.srt | 13 KB | ||
| 018. Decision Trees.mp4 | 37.8 MB | ||
| 018. Decision Trees.srt | 28.4 KB | ||
| 019. Model Evaluation & Selection.mp4 | 46.1 MB | ||
| 019. Model Evaluation & Selection.srt | 30.1 KB | ||
| 020. Confusion Matrices & Basic Evaluation Metrics.mp4 | 20.8 MB | ||
| 020. Confusion Matrices & Basic Evaluation Metrics.srt | 15.8 KB | ||
| 021. Classifier Decision Functions.mp4 | 12.7 MB | ||
| 021. Classifier Decision Functions.srt | 9 KB | ||
| 022. Precision-recall and ROC curves.mp4 | 9.2 MB | ||
| 022. Precision-recall and ROC curves.srt | 7.5 KB | ||
| 023. Multi-Class Evaluation.mp4 | 19.8 MB | ||
| 023. Multi-Class Evaluation.srt | 15.2 KB | ||
| 024. Regression Evaluation.mp4 | 17 MB | ||
| 024. Regression Evaluation.srt | 7.8 KB | ||
| 025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4 | 34.5 MB | ||
| 025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.srt | 18.1 KB | ||
| 026. Naive Bayes Classifiers.mp4 | 21.4 MB | ||
| 026. Naive Bayes Classifiers.srt | 11.2 KB | ||
| 027. Random Forests.mp4 | 26.4 MB | ||
| 027. Random Forests.srt | 17.1 KB | ||
| 028. Gradient Boosted Decision Trees.mp4 | 11.8 MB | ||
| 028. Gradient Boosted Decision Trees.srt | 8.4 KB | ||
| 029. Neural Networks.mp4 | 41.5 MB | ||
| 029. Neural Networks.srt | 27.9 KB | ||
| 030. Deep Learning (Optional).mp4 | 17.5 MB | ||
| 030. Deep Learning (Optional).srt | 10.3 KB | ||
| 031. Data Leakage.mp4 | 32.9 MB | ||
| 031. Data Leakage.srt | 16.7 KB | ||
| 032. Introduction.mp4 | 10.7 MB | ||
| 032. Introduction.srt | 6.5 KB | ||
| 033. Dimensionality Reduction and Manifold Learning.mp4 | 16.1 MB | ||
| 033. Dimensionality Reduction and Manifold Learning.srt | 13.5 KB | ||
| 034. Clustering.mp4 | 27.2 MB | ||
| 034. Clustering.srt | 19.9 KB | ||
| 035. Conclusion.mp4 | 9.9 MB | ||
| 035. Conclusion.srt | 3.9 KB | ||
| [FTU Forum].url | 204.8 B | ||
| [FreeCoursesOnline.Me].url | 102.4 B | ||
| [FreeTutorials.Us].url | 102.4 B | ||
| ▲ 73 total files | |||
[COURSERA] APPLIED MACHINE LEARNING IN PYTHON [FCO]
About this course: This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
For More Udemy Free Courses >>> http://www.freetutorials.us
For more Coursera Courses >>> https://www.freecoursesonline.me/
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1.3 GB | freecoursewb | 4 weeks | 14 | 2 | |
|
[Coursera] Applied Python Data Engineering Specialization - 3 course series Posted by
SunRiseZone in Other
|
2.1 GB | SunRiseZone | 2 years | 19 | 4 |
| 623.3 MB | SunRiseZone | 2 years | 6 | 0 | |
| 553 MB | crackzsoft | 3 years | 0 | 0 | |
| 2 GB | tutsnode | 5 years | 0 | 0 |
All Comments