[Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Linear Regression in Python

Creation Time	Sept. 16, 2023, 7:02 a.m.
Last Access Time	Sept. 20, 2024, 8:54 a.m.
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Keywords	Tutorialsplanet Linear Prerequisites - Deep Udemy Python Learning in NET Regression
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Total Files	102
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6. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 186.3 MB
6. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 60.3 MB
6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 59.4 MB
1. Welcome/1. Welcome.mp4 49.7 MB
6. Appendix FAQ/11. What order should I take your courses in (part 2).srt 47.4 MB
6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
4. Practical machine learning issues/3. Generalization error, train and test sets.srt 39.0 MB
6. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39.0 MB
6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 37.8 MB
6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 37.6 MB
6. Appendix FAQ/10. What order should I take your courses in (part 1).srt 29.3 MB
6. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 29.3 MB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 24.7 MB
6. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 24.5 MB
4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 23.5 MB
4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 22.8 MB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 19.7 MB
6. Appendix FAQ/6. How to Code by Yourself (part 2).srt 19.5 MB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 19.3 MB
6. Appendix FAQ/7. How to Succeed in this Course (Long Version).mp4 18.3 MB
2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.mp4 17.5 MB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp4 17.2 MB
3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).mp4 16.4 MB
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp4 14.9 MB
6. Appendix FAQ/6. How to Code by Yourself (part 2).mp4 14.8 MB
2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.mp4 14.4 MB
4. Practical machine learning issues/2. Interpreting the Weights.mp4 14.1 MB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).srt 13.8 MB
3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.mp4 12.3 MB
4. Practical machine learning issues/1. What do all these letters mean.mp4 9.6 MB
4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.mp4 8.6 MB
4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.mp4 8.5 MB
1. Welcome/3. What is machine learning How does linear regression play a role.mp4 8.4 MB
4. Practical machine learning issues/15. L1 Regularization - Code.mp4 8.3 MB
4. Practical machine learning issues/5. Categorical inputs.mp4 8.2 MB
5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.mp4 8.1 MB
4. Practical machine learning issues/9. L2 Regularization - Code.mp4 8.1 MB
6. Appendix FAQ/12. Python 2 vs Python 3.mp4 7.8 MB
4. Practical machine learning issues/16. L1 vs L2 Regularization.srt 7.6 MB
5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.mp4 7.2 MB
4. Practical machine learning issues/8. L2 Regularization - Theory.srt 6.7 MB
4. Practical machine learning issues/8. L2 Regularization - Theory.mp4 6.7 MB
1. Welcome/2. Introduction and Outline.mp4 6.3 MB
4. Practical machine learning issues/10. The Dummy Variable Trap.mp4 6.1 MB
6. Appendix FAQ/1. What is the Appendix.mp4 5.5 MB
4. Practical machine learning issues/16. L1 vs L2 Regularization.mp4 4.8 MB
4. Practical machine learning issues/14. L1 Regularization - Theory.mp4 4.7 MB
2. 1-D Linear Regression Theory and Code/6. R-squared in code.mp4 4.5 MB
1. Welcome/4. Introduction to Moore's Law Problem.mp4 4.4 MB
4. Practical machine learning issues/3. Generalization error, train and test sets.mp4 4.4 MB
4. Practical machine learning issues/6. One-Hot Encoding Quiz.mp4 3.8 MB
4. Practical machine learning issues/12. Gradient Descent for Linear Regression.mp4 3.5 MB
3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.mp4 3.5 MB
1. Welcome/6. How to Succeed in this Course.mp4 3.3 MB
3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.mp4 3.1 MB
2. 1-D Linear Regression Theory and Code/8. R-squared Quiz 1.mp4 2.8 MB
2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.mp4 1.0 MB
6. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 31.8 KB
6. Appendix FAQ/5. How to Code by Yourself (part 1).srt 22.8 KB
6. Appendix FAQ/3. Windows-Focused Environment Setup 2018.srt 20.1 KB
6. Appendix FAQ/7. How to Succeed in this Course (Long Version).srt 14.5 KB
6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.5 KB
6. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.srt 14.1 KB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.srt 12.9 KB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.srt 11.1 KB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.srt 9.2 KB
4. Practical machine learning issues/17. Why Divide by Square Root of D.srt 8.7 KB
4. Practical machine learning issues/1. What do all these letters mean.srt 8.0 KB
2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.srt 6.9 KB
4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.srt 6.4 KB
6. Appendix FAQ/12. Python 2 vs Python 3.srt 6.1 KB
1. Welcome/2. Introduction and Outline.srt 5.9 KB
1. Welcome/3. What is machine learning How does linear regression play a role.srt 5.8 KB
5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.srt 5.7 KB
2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.srt 5.6 KB
4. Practical machine learning issues/11. Gradient Descent Tutorial.srt 5.5 KB
4. Practical machine learning issues/10. The Dummy Variable Trap.srt 5.5 KB
3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.srt 5.5 KB
5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.srt 5.3 KB
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.srt 5.2 KB
3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).srt 4.9 KB
4. Practical machine learning issues/5. Categorical inputs.srt 4.8 KB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.srt 4.7 KB
1. Welcome/1. Welcome.srt 4.5 KB
4. Practical machine learning issues/2. Interpreting the Weights.srt 4.3 KB
4. Practical machine learning issues/14. L1 Regularization - Theory.srt 4.1 KB
1. Welcome/6. How to Succeed in this Course.srt 4.0 KB
1. Welcome/4. Introduction to Moore's Law Problem.srt 3.7 KB
6. Appendix FAQ/1. What is the Appendix.srt 3.7 KB
4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.srt 3.6 KB
4. Practical machine learning issues/15. L1 Regularization - Code.srt 3.5 KB
4. Practical machine learning issues/9. L2 Regularization - Code.srt 3.4 KB
4. Practical machine learning issues/12. Gradient Descent for Linear Regression.srt 3.1 KB
3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.srt 2.7 KB
4. Practical machine learning issues/6. One-Hot Encoding Quiz.srt 2.5 KB
2. 1-D Linear Regression Theory and Code/8. R-squared Quiz 1.srt 2.2 KB
3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.srt 2.0 KB
2. 1-D Linear Regression Theory and Code/6. R-squared in code.srt 1.7 KB
2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.srt 1.6 KB
1. Welcome/5. What can linear regression be used for.html 150 bytes
[Tutorialsplanet.NET].url 128 bytes

[Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Linear Regression in Python

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