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6. Linear Regression/18. Ridge regression and Lasso in Python.mp4 264.3 MB
4. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 129.9 MB
5. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4 119.3 MB
6. Linear Regression/5. Assessing accuracy of predicted coefficients.mp4 109.5 MB
6. Linear Regression/10. Multiple Linear Regression in Python.mp4 92.4 MB
6. Linear Regression/16. Subset selection techniques.mp4 91.4 MB
5. Data Preprocessing/10. Outlier Treatment in Python.mp4 90.8 MB
2. Basics of Statistics/3. Describing data Graphically.mp4 86.2 MB
5. Data Preprocessing/23. Correlation Analysis.mp4 85.2 MB
3. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp4 84.5 MB
6. Linear Regression/3. Simple Linear Regression in Python.mp4 82.5 MB
5. Data Preprocessing/3. The Dataset and the Data Dictionary.mp4 82.4 MB
5. Data Preprocessing/7. EDD in Python.mp4 78.7 MB
3. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp4 77.3 MB
3. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp4 76.6 MB
5. Data Preprocessing/24. Correlation Analysis in Python.mp4 71.3 MB
6. Linear Regression/8. The F - statistic.mp4 67.2 MB
1. Introduction/2. Course contents.mp4 67.0 MB
6. Linear Regression/14. Test train split in Python.mp4 60.6 MB
3. Setting up Python and Jupyter Notebook/8. Working with Panda Library of Python.mp4 59.2 MB

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L'uomo che visse nel futuro - The Time Machine (1960).1080p.ita.eng.sub.ita.eng-MIRCrew mux by robbyrs.mkv 2.4 GB
L'uomo che visse nel futuro - The Time Machine (1960).1080p.ita.eng.sub.ita.eng-MIRCrew mux by robbyrs.eng.srt 62.3 kB
L'uomo che visse nel futuro - The Time Machine (1960).1080p.ita.eng.sub.ita.eng-MIRCrew mux by robbyrs.ita.srt 55.6 kB
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6. Linear Regression Model/20. Ridge regression and Lasso in R.mp4 189.9 MB
4. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4 129.9 MB
3. Getting started with R and R studio/7. Creating Barplots in R.mp4 123.3 MB
5. Data Preprocessing/16. Bi-variate Analysis and Variable Transformation.mp4 119.3 MB
5. Data Preprocessing/7. EDD in R.mp4 117.7 MB
6. Linear Regression Model/3. Assessing Accuracy of predicted coefficients.mp4 109.5 MB
3. Getting started with R and R studio/3. Packages in R.mp4 103.5 MB
5. Data Preprocessing/24. Correlation Matrix in R.mp4 99.7 MB
6. Linear Regression Model/14. Test-Train Split in R.mp4 95.5 MB
6. Linear Regression Model/16. Subset Selection techniques.mp4 91.3 MB
2. Basics of Statistics/3. Describing the data graphically.mp4 86.2 MB
5. Data Preprocessing/23. Correlation Matrix and cause-effect relationship.mp4 85.2 MB
5. Data Preprocessing/3. The Data and the Data Dictionary.mp4 82.4 MB
6. Linear Regression Model/17. Subset selection in R.mp4 80.3 MB
6. Linear Regression Model/10. Multiple Linear Regression in R.mp4 76.7 MB
3. Getting started with R and R studio/6. Inputting data part 3 Importing from CSV or Text files.mp4 72.5 MB
5. Data Preprocessing/17. Variable transformation in R.mp4 71.2 MB
6. Linear Regression Model/8. The F - statistic.mp4 67.3 MB
1. Introduction/2. Course contents.mp4 66.6 MB
6. Linear Regression Model/19. Shrinkage methods - Ridge Regression and The Lasso.mp4 55.5 MB

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6. Appendix/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.mp4 50.0 MB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.mp4 43.8 MB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
6. Appendix/13. What order should I take your courses in (part 2).mp4 39.4 MB
6. Appendix/12. What order should I take your courses in (part 1).mp4 30.7 MB
6. Appendix/5. How to Code by Yourself (part 1).mp4 25.7 MB
3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.mp4 21.3 MB
3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.mp4 16.6 MB
5. AdaBoost/4. AdaBoost Implementation.mp4 16.5 MB
4. Random Forest/2. Random Forest Regressor.mp4 15.6 MB
6. Appendix/6. How to Code by Yourself (part 2).mp4 15.5 MB
4. Random Forest/1. Random Forest Algorithm.mp4 15.1 MB
2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.mp4 14.5 MB
6. Appendix/7. How to Succeed in this Course (Long Version).mp4 13.6 MB
6. Appendix/2. Confidence Intervals.mp4 13.2 MB
4. Random Forest/3. Random Forest Classifier.mp4 13.2 MB
5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.mp4 11.7 MB