- 【影视】 [ FreeCourseWeb.com ] Udemy - Machine Learning with SciKit-Learn with Python
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- ~Get Your Files Here !/9. Cross Validation/13. Performance Analysis on the Test Set.mp4 125.2 MB
- ~Get Your Files Here !/6. Pandas/6. Remove Operations.mp4 123.1 MB
- ~Get Your Files Here !/9. Cross Validation/5. PCA Pipeline.mp4 122.9 MB
- ~Get Your Files Here !/9. Cross Validation/1. Cross Validation.mp4 117.4 MB
- ~Get Your Files Here !/5. Matlplotlib/3. Multiple Figures and Subplots.mp4 115.7 MB
- ~Get Your Files Here !/8. Learning and Predicting/4. Persisting Models.mp4 110.3 MB
- ~Get Your Files Here !/9. Cross Validation/3. K-Means Clustering Example.mp4 109.0 MB
- ~Get Your Files Here !/6. Pandas/7. Pandas Arithmetic Operations.mp4 108.2 MB
- ~Get Your Files Here !/6. Pandas/4. Data Structure in Pandas Continue.mp4 107.1 MB
- ~Get Your Files Here !/8. Learning and Predicting/5. K-NN Algorithm with Example.mp4 106.3 MB
- ~Get Your Files Here !/3. NumPy Array/6. Numpy Array Splicing.mp4 103.6 MB
- ~Get Your Files Here !/3. NumPy Array/4. NumPy Array Operations Continue.mp4 97.5 MB
- ~Get Your Files Here !/9. Cross Validation/15. Language Identifcation.mp4 97.4 MB
- ~Get Your Files Here !/9. Cross Validation/9. Text Data Example.mp4 94.2 MB
- ~Get Your Files Here !/3. NumPy Array/8. Stacking Together Different Arrays.mp4 89.7 MB
- ~Get Your Files Here !/5. Matlplotlib/2. Understanding Various Functions of Pyplot.mp4 89.6 MB
- ~Get Your Files Here !/9. Cross Validation/14. Parameter Tuning.mp4 89.3 MB
- ~Get Your Files Here !/6. Pandas/3. Data Structure in Pandas.mp4 88.0 MB
- ~Get Your Files Here !/6. Pandas/5. Pandas Column Select.mp4 83.4 MB
- ~Get Your Files Here !/3. NumPy Array/3. NumPy Array Operations.mp4 82.3 MB
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- 05-Implement a Naive Bayes Classifier in Python and Scikit-learn to Categorize Text.mp4 16.9 MB
- 06-Implement Decision Trees in Python with Scikit-learn.mp4 16.6 MB
- 01-Use Linear Regression To Estimate Continuous Values with Python and Scikit-learn.mp4 14.0 MB
- 02-Predict Categories in Python using Scikit-learn's Logistic Regression module.mp4 12.4 MB
- 04-Use K-nearest Neighbors To Find Similar Datapoints with Python and Scikit-learn.mp4 11.3 MB
- 07-Find Clusters of Data with K-means Clustering in Python and Scikit-learn.mp4 9.0 MB
- 03-Use Support Vector Machines To Find Complex Decision Boundaries with Scikit-learn.mp4 8.2 MB
- Seed Me!!.png 43.1 kB
- AhLaN.txt 961 Bytes
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- 9. Cross Validation/13. Performance Analysis on the Test Set.mp4 125.2 MB
- 6. Pandas/6. Remove Operations.mp4 123.1 MB
- 9. Cross Validation/5. PCA Pipeline.mp4 122.9 MB
- 9. Cross Validation/1. Cross Validation.mp4 117.4 MB
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- 8. Learning and Predicting/4. Persisting Models.mp4 110.3 MB
- 9. Cross Validation/3. K-Means Clustering Example.mp4 109.0 MB
- 6. Pandas/7. Pandas Arithmetic Operations.mp4 108.2 MB
- 6. Pandas/4. Data Structure in Pandas Continue.mp4 107.1 MB
- 8. Learning and Predicting/5. K-NN Algorithm with Example.mp4 106.3 MB
- 3. NumPy Array/6. Numpy Array Splicing.mp4 103.6 MB
- 3. NumPy Array/4. NumPy Array Operations Continue.mp4 97.5 MB
- 9. Cross Validation/15. Language Identifcation.mp4 97.4 MB
- 9. Cross Validation/9. Text Data Example.mp4 94.2 MB
- 3. NumPy Array/8. Stacking Together Different Arrays.mp4 89.7 MB
- 5. Matlplotlib/2. Understanding Various Functions of Pyplot.mp4 89.6 MB
- 9. Cross Validation/14. Parameter Tuning.mp4 89.3 MB
- 6. Pandas/3. Data Structure in Pandas.mp4 88.0 MB
- 6. Pandas/5. Pandas Column Select.mp4 83.4 MB
- 3. NumPy Array/3. NumPy Array Operations.mp4 82.3 MB
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- 4. Segmentation and Transformation/10. Comparing Images Using MSE and SSIM.mp4 23.2 MB
- exercise.7z 21.8 MB
- 3. Object and Feature Detection/7. Applying Denoising Filters.mp4 17.2 MB
- 3. Object and Feature Detection/9. Filling Holes and Finding Peaks Using Erosion and Dilation.mp4 17.0 MB
- 2. Working with Image Data/05. Working with Images as NumPy Arrays.mp4 14.3 MB
- 4. Segmentation and Transformation/03. Applying Global and Local Thresholding Algorithms.mp4 12.2 MB
- 4. Segmentation and Transformation/05. Segmentation and Merging Segments Using Rags.mp4 11.9 MB
- 2. Working with Image Data/14. Canny Edge Detection.mp4 11.1 MB
- 2. Working with Image Data/10. Contours.mp4 10.6 MB
- 4. Segmentation and Transformation/08. Applying Image Transformations.mp4 9.9 MB
- 3. Object and Feature Detection/5. Corner Detection.mp4 9.7 MB
- 3. Object and Feature Detection/4. Visualizing Hog Feature Descriptors.mp4 9.6 MB
- 3. Object and Feature Detection/3. Visualizing Daisy Descriptors on Images.mp4 9.5 MB
- 2. Working with Image Data/09. Block Views and Pooling Operations.mp4 9.1 MB
- 2. Working with Image Data/07. Masking Color Images.mp4 8.4 MB
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- 2. Working with Image Data/06. Masking Images Using Array Manipulation.mp4 7.8 MB
- 2. Working with Image Data/04. Introducing scikit-image.mp4 6.6 MB
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- 1.Predicting the Wine Quality Using Multiple Linear Regression/05.Evaluating and Improving the Model.mp4 40.7 MB
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- 4.Poker Hand Predictions with K-Means Clustering/20.Implementing the Solution.mp4 27.6 MB
- 2.Bike Sharing Demand Prediction Using Regression Trees/10.Implementing the Solution.mp4 25.5 MB
- 5.Understanding Buying Behavior Using Hierarchical Clustering/25.Implementing the Solution.mp4 22.8 MB
- 4.Poker Hand Predictions with K-Means Clustering/21.Analyze the Results.mp4 22.7 MB
- 5.Understanding Buying Behavior Using Hierarchical Clustering/24.Feature Analysis and Engineering.mp4 22.6 MB
- 4.Poker Hand Predictions with K-Means Clustering/18.K-Means Clustering.mp4 19.3 MB
- 2.Bike Sharing Demand Prediction Using Regression Trees/08.Decision Trees and Random Forest.mp4 17.3 MB
- 2.Bike Sharing Demand Prediction Using Regression Trees/11.Analyze the Results.mp4 16.0 MB
- 5.Understanding Buying Behavior Using Hierarchical Clustering/26.Analyze the Results.mp4 15.2 MB
- 3.Heart Disease Predictions with Support Vector Machines/16.Analyze the Results.mp4 13.4 MB
- 1.Predicting the Wine Quality Using Multiple Linear Regression/06.Analyzing the Results.mp4 13.2 MB
- 1.Predicting the Wine Quality Using Multiple Linear Regression/03.Multiple Linear Regression.mp4 13.2 MB
- 5.Understanding Buying Behavior Using Hierarchical Clustering/23.Hierarchical Clustering.mp4 12.8 MB
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- ~Get Your Files Here !/459 - TTS - 4-2 - A Larger Example.mp4 37.0 MB
- ~Get Your Files Here !/459 - TTS - 3-2 - Reproducible Results.mp4 16.0 MB
- ~Get Your Files Here !/459 - TTS - 3-1 - Using TTS.mp4 15.3 MB
- ~Get Your Files Here !/459 - TTS - 4-1 - A Small Examples.mp4 13.8 MB
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- ~Get Your Files Here !/459 - TTS - 2 - Installing SKLearn.mp4 7.3 MB
- ~Get Your Files Here !/splitting-datasets-course-slides.pdf 6.5 MB
- ~Get Your Files Here !/459 - TTS - 5 - Other Validation Methods.mp4 6.1 MB
- ~Get Your Files Here !/459 - TTS - 6 - Summary.mp4 4.9 MB
- ~Get Your Files Here !/459 - TTS - 0 - Intro.mp4 3.5 MB
- ~Get Your Files Here !/459 - TTS - 4 - Supervised Learning.mp4 1.5 MB
- ~Get Your Files Here !/459 - TTS - 3 - Application of TTS.mp4 1.2 MB
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- ~Get Your Files Here !/2. Scaling Data Part 1.mp4 136.4 MB
- ~Get Your Files Here !/3. Scaling Data Part 2.mp4 132.6 MB
- ~Get Your Files Here !/4. Dealing with Categorical Variables.mp4 107.8 MB
- ~Get Your Files Here !/5. Dealing with Missing Values.mp4 93.0 MB
- ~Get Your Files Here !/1. Introduction.mp4 83.7 MB
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- ~Get Your Files Here !/4. Dealing with Categorical Variables.srt 17.9 kB
- ~Get Your Files Here !/2. Scaling Data Part 1.srt 17.2 kB
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- ~Get Your Files Here !/3. Scaling Data Part 2.srt 11.5 kB
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- 9. Cross Validation/13. Performance Analysis on the Test Set.mp4 125.2 MB
- 6. Pandas/6. Remove Operations.mp4 123.1 MB
- 9. Cross Validation/5. PCA Pipeline.mp4 122.9 MB
- 9. Cross Validation/1. Cross Validation.mp4 117.4 MB
- 5. Matlplotlib/3. Multiple Figures and Subplots.mp4 115.7 MB
- 8. Learning and Predicting/4. Persisting Models.mp4 110.3 MB
- 9. Cross Validation/3. K-Means Clustering Example.mp4 109.0 MB
- 6. Pandas/7. Pandas Arithmetic Operations.mp4 108.2 MB
- 6. Pandas/4. Data Structure in Pandas Continue.mp4 107.1 MB
- 8. Learning and Predicting/5. K-NN Algorithm with Example.mp4 106.3 MB
- 3. NumPy Array/6. Numpy Array Splicing.mp4 103.6 MB
- 3. NumPy Array/4. NumPy Array Operations Continue.mp4 97.5 MB
- 9. Cross Validation/15. Language Identifcation.mp4 97.4 MB
- 9. Cross Validation/9. Text Data Example.mp4 94.2 MB
- 3. NumPy Array/8. Stacking Together Different Arrays.mp4 89.7 MB
- 5. Matlplotlib/2. Understanding Various Functions of Pyplot.mp4 89.6 MB
- 9. Cross Validation/14. Parameter Tuning.mp4 89.3 MB
- 6. Pandas/3. Data Structure in Pandas.mp4 88.0 MB
- 6. Pandas/5. Pandas Column Select.mp4 83.4 MB
- 3. NumPy Array/3. NumPy Array Operations.mp4 82.3 MB
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- 收录时间:2021-08-17 文档个数:110 文档大小:3.9 GB 最近下载:2024-11-14 人气:747 磁力链接
- 9. Cross Validation/13. Performance Analysis on the Test Set.mp4 125.2 MB
- 6. Pandas/6. Remove Operations.mp4 123.1 MB
- 9. Cross Validation/5. PCA Pipeline.mp4 122.9 MB
- 9. Cross Validation/1. Cross Validation.mp4 117.4 MB
- 5. Matlplotlib/3. Multiple Figures and Subplots.mp4 115.7 MB
- 8. Learning and Predicting/4. Persisting Models.mp4 110.3 MB
- 9. Cross Validation/3. K-Means Clustering Example.mp4 109.0 MB
- 6. Pandas/7. Pandas Arithmetic Operations.mp4 108.2 MB
- 6. Pandas/4. Data Structure in Pandas Continue.mp4 107.1 MB
- 8. Learning and Predicting/5. K-NN Algorithm with Example.mp4 106.3 MB
- 3. NumPy Array/6. Numpy Array Splicing.mp4 103.6 MB
- 3. NumPy Array/4. NumPy Array Operations Continue.mp4 97.5 MB
- 9. Cross Validation/15. Language Identifcation.mp4 97.4 MB
- 9. Cross Validation/9. Text Data Example.mp4 94.2 MB
- 3. NumPy Array/8. Stacking Together Different Arrays.mp4 89.7 MB
- 5. Matlplotlib/2. Understanding Various Functions of Pyplot.mp4 89.6 MB
- 9. Cross Validation/14. Parameter Tuning.mp4 89.3 MB
- 6. Pandas/3. Data Structure in Pandas.mp4 88.0 MB
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- ~Get Your Files Here !/2. Linear Regression in scikit-learn.mp4 173.7 MB
- ~Get Your Files Here !/5. Classification in scikit-learn.mp4 112.0 MB
- ~Get Your Files Here !/4. Shrinkage Models in scikit-learn.mp4 102.1 MB
- ~Get Your Files Here !/1. Introduction.mp4 63.7 MB
- ~Get Your Files Here !/3. The Bias-Variance Trade Off.mp4 58.8 MB
- ~Get Your Files Here !/6. Conclusion.mp4 17.6 MB
- ~Get Your Files Here !/2. Linear Regression in scikit-learn.srt 30.6 kB
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- ~Get Your Files Here !/5. Classification in scikit-learn.srt 19.6 kB
- ~Get Your Files Here !/3. The Bias-Variance Trade Off.srt 10.8 kB
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- ~Get Your Files Here !/6. Conclusion.srt 2.1 kB
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- 6. Logistic Regression/3. What is the Difference Between a Logistic and a Logit Function.mp4 79.5 MB
- 7. Cluster Analysis/17. What Can be Done with Cluster Analysis.mp4 66.6 MB
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- 6. Logistic Regression/14. Underfitting and Overfitting.srt 62.2 MB
- 5. Linear Regression - Practical Example/8. Practical Example (Part 5).mp4 60.7 MB
- 5. Linear Regression - Practical Example/6. Practical Example (Part 4).mp4 58.7 MB
- 1. Introduction/1. What Does the Course Cover.mp4 54.0 MB
- 3. Linear Regression with StatsModels/38. Dealing with Categorical Data.mp4 53.9 MB
- 3. Linear Regression with StatsModels/3. The Linear Regression Model.mp4 53.5 MB
- 7. Cluster Analysis/16. Practical Example Market Segmentation (Part 2).mp4 53.3 MB
- 3. Linear Regression with StatsModels/23. Adjusted R-Squared.mp4 52.1 MB
- 4. Linear Regression with Sklearn/19. Training and Testing.mp4 51.6 MB
- 7. Cluster Analysis/6. A Hands on Example of K-Means.mp4 51.0 MB
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- 7. Cluster Analysis/1. Introduction to Cluster Analysis.mp4 46.8 MB
- 3. Linear Regression with StatsModels/15. SST, SSR, and SSE.mp4 44.3 MB
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- 6. Logistic Regression/3. What is the Difference Between a Logistic and a Logit Function.mp4 79.5 MB
- 7. Cluster Analysis/17. What Can be Done with Cluster Analysis.mp4 66.6 MB
- 7. Cluster Analysis/2. Examples of Clustering.mp4 66.0 MB
- 6. Logistic Regression/14. Underfitting and Overfitting.srt 62.2 MB
- 5. Linear Regression - Practical Example/8. Practical Example (Part 5).mp4 60.7 MB
- 5. Linear Regression - Practical Example/6. Practical Example (Part 4).mp4 58.7 MB
- 1. Introduction/1. What Does the Course Cover.mp4 54.0 MB
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- 7. Cluster Analysis/16. Practical Example Market Segmentation (Part 2).mp4 53.3 MB
- 3. Linear Regression with StatsModels/23. Adjusted R-Squared.mp4 52.1 MB
- 4. Linear Regression with Sklearn/19. Training and Testing.mp4 51.6 MB
- 7. Cluster Analysis/6. A Hands on Example of K-Means.mp4 51.0 MB
- 5. Linear Regression - Practical Example/2. Practical Example (Part 2).mp4 48.2 MB
- 7. Cluster Analysis/1. Introduction to Cluster Analysis.mp4 46.8 MB
- 3. Linear Regression with StatsModels/15. SST, SSR, and SSE.mp4 44.3 MB
- 3. Linear Regression with StatsModels/10. Simple Linear Regression in Python.mp4 43.8 MB
- 2. Setting Up The Working Environment/2. Why Python and Why Jupyter.mp4 43.1 MB
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- 6. Logistic Regression/3. What is the Difference Between a Logistic and a Logit Function.mp4 79.5 MB
- 7. Cluster Analysis/17. What Can be Done with Cluster Analysis.mp4 66.6 MB
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- 6. Logistic Regression/14. Underfitting and Overfitting.srt 62.2 MB
- 5. Linear Regression - Practical Example/8. Practical Example (Part 5).mp4 60.7 MB
- 5. Linear Regression - Practical Example/6. Practical Example (Part 4).mp4 58.7 MB
- 1. Introduction/1. What Does the Course Cover.mp4 54.0 MB
- 3. Linear Regression with StatsModels/38. Dealing with Categorical Data.mp4 53.9 MB
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- 7. Cluster Analysis/16. Practical Example Market Segmentation (Part 2).mp4 53.3 MB
- 3. Linear Regression with StatsModels/23. Adjusted R-Squared.mp4 52.1 MB
- 4. Linear Regression with Sklearn/19. Training and Testing.mp4 51.6 MB
- 7. Cluster Analysis/6. A Hands on Example of K-Means.mp4 51.0 MB
- 5. Linear Regression - Practical Example/2. Practical Example (Part 2).mp4 48.2 MB
- 7. Cluster Analysis/1. Introduction to Cluster Analysis.mp4 46.8 MB
- 3. Linear Regression with StatsModels/15. SST, SSR, and SSE.mp4 44.3 MB
- 3. Linear Regression with StatsModels/10. Simple Linear Regression in Python.mp4 43.8 MB
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