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种子名称： [GigaCourse.Com] Udemy - Machine Learning with SciKit-Learn with Python
文件类型： 视频
文件数目： 54个文件
文件大小： 3.59 GB
收录时间： 2021-10-15 00:58
已经下载： 3次
资源热度： 166
最近下载： 2024-11-14 03:17

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[GigaCourse.Com] Udemy - Machine Learning with SciKit-Learn with Python.torrent

1. Introduction/1. Introduction to Machine Learning.mp427.81MB

1. Introduction/2. Advantages and Disadvantages of Machine Learning.mp426.95MB

10. Movie Review Analysis/1. Movie Review Screen Stream.mp467.84MB

10. Movie Review Analysis/2. Movie Review Screen Stream Continue.mp437.62MB

2. NumPy/1. NumPy Introduction.mp435.77MB

2. NumPy/2. Features and Installation.mp456.83MB

3. NumPy Array/1. NumPy Array Creation.mp455.2MB

3. NumPy Array/10. Copies and Views.mp456.09MB

3. NumPy Array/2. NumPy Array Attributes.mp456.02MB

3. NumPy Array/3. NumPy Array Operations.mp478.5MB

3. NumPy Array/4. NumPy Array Operations Continue.mp493MB

3. NumPy Array/5. NumPy Array Unary Operations.mp435.53MB

3. NumPy Array/6. Numpy Array Splicing.mp498.85MB

3. NumPy Array/7. NumPy Array Shpe.mp467.32MB

3. NumPy Array/8. Stacking Together Different Arrays.mp485.55MB

3. NumPy Array/9. Splitting one Array into Several Smaller ones.mp443.73MB

4. Indexing Arrays of Arrays/1. NumPy Array Indexing.mp461.75MB

4. Indexing Arrays of Arrays/2. NumPy Array Indexing Continue.mp435.43MB

4. Indexing Arrays of Arrays/3. NumPy Array Boolean.mp466.86MB

5. Matlplotlib/1. Introduction to Matlplotlib.mp427.49MB

5. Matlplotlib/2. Understanding Various Functions of Pyplot.mp485.44MB

5. Matlplotlib/3. Multiple Figures and Subplots.mp4110.33MB

6. Pandas/1. Intro to Pandas.mp437.52MB

6. Pandas/2. Intro to Pandas Continue.mp442.55MB

6. Pandas/3. Data Structure in Pandas.mp483.95MB

6. Pandas/4. Data Structure in Pandas Continue.mp4102.17MB

6. Pandas/5. Pandas Column Select.mp479.57MB

6. Pandas/6. Remove Operations.mp4117.42MB

6. Pandas/7. Pandas Arithmetic Operations.mp4103.23MB

6. Pandas/8. Pandas Arithmetic Operations Continue.mp449.67MB

7. Scikit Learn/1. Introduction to Scikit Learn.mp448.75MB

7. Scikit Learn/2. Supervised.mp452.55MB

7. Scikit Learn/3. Unsupervised Learning.mp453.29MB

7. Scikit Learn/4. Load Data Set.mp443.44MB

8. Learning and Predicting/1. Scikit Example Digits.mp445.2MB

8. Learning and Predicting/2. Digits Dataset Using Matplotlib.mp462.8MB

8. Learning and Predicting/3. Understading Metrics of Predicted Digits Dataset.mp441.99MB

8. Learning and Predicting/4. Persisting Models.mp4105.18MB

8. Learning and Predicting/5. K-NN Algorithm with Example.mp4101.37MB

9. Cross Validation/1. Cross Validation.mp4112.01MB

9. Cross Validation/10. Extracting Features.mp446.9MB

9. Cross Validation/11. Occurrences to Frequencies.mp477.07MB

9. Cross Validation/12. Classifier Training.mp460.97MB

9. Cross Validation/13. Performance Analysis on the Test Set.mp4119.41MB

9. Cross Validation/14. Parameter Tuning.mp485.14MB

9. Cross Validation/15. Language Identifcation.mp492.92MB

9. Cross Validation/2. Cross Validation Techniques.mp452.18MB

9. Cross Validation/3. K-Means Clustering Example.mp4103.98MB

9. Cross Validation/4. Agglomeration.mp476.56MB

9. Cross Validation/5. PCA Pipeline.mp4117.16MB

9. Cross Validation/6. Face Recognition.mp453.62MB

9. Cross Validation/7. Face Recognition Output.mp452.15MB

9. Cross Validation/8. Right Estimator.mp459.87MB

9. Cross Validation/9. Text Data Example.mp489.82MB