Udemy - Deep Learning Prerequisites Linear Regression in Python (1.2021)

Creation Time	July 5, 2025, 8:35 p.m.
Last Access Time	July 6, 2025, 5:41 p.m.
File Size	1.1 GB
Keywords	Linear Prerequisites - Deep Udemy 1 Python 2021 Learning in Regression
Total Requests	4
Total Files	109
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6. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4 186.3 MB
1. Welcome/4. Anyone Can Succeed in this Course.mp4 84.0 MB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
1. Welcome/5. Statistics vs. Machine Learning.mp4 55.5 MB
1. Welcome/1. Welcome.mp4 49.7 MB
6. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
8. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39.0 MB
9. Appendix FAQ Finale/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 37.8 MB
8. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 37.6 MB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 36.1 MB
8. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 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
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. 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/9. Moore's Law Derivation.mp4 20.2 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
8. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 18.3 MB
2. 1-D Linear Regression Theory and Code/8. 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
2. 1-D Linear Regression Theory and Code/11. Suggestion Box.mp4 16.1 MB
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp4 14.9 MB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).mp4 14.8 MB
3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).mp4 14.4 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 14.2 MB
3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.mp4 12.3 MB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 11.3 MB
4. Practical machine learning issues/1. What do all these letters mean.mp4 9.6 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/7. Probabilistic Interpretation of Squared Error.mp4 8.1 MB
4. Practical machine learning issues/9. L2 Regularization - Code.mp4 8.1 MB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.mp4 7.8 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.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
9. Appendix FAQ Finale/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
2. 1-D Linear Regression Theory and Code/7. 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
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/10. 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
8. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 33.8 KB
8. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 25.2 KB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).srt 24.3 KB
6. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.srt 21.6 KB
1. Welcome/4. Anyone Can Succeed in this Course.srt 19.1 KB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).srt 17.7 KB
8. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 17.2 KB
1. Welcome/5. Statistics vs. Machine Learning.srt 16.0 KB
6. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15.6 KB
8. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).srt 15.2 KB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.srt 15.1 KB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).srt 13.9 KB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.srt 12.9 KB
3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).srt 12.5 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/17. Why Divide by Square Root of D.srt 9.4 KB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.srt 9.2 KB
4. Practical machine learning issues/1. What do all these letters mean.srt 8.5 KB
9. Appendix FAQ Finale/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 8.4 KB
2. 1-D Linear Regression Theory and Code/9. Moore's Law Derivation.srt 8.1 KB
2. 1-D Linear Regression Theory and Code/8. Demonstrating Moore's Law in Code.srt 6.9 KB
4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.srt 6.8 KB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.srt 6.5 KB
4. Practical machine learning issues/8. L2 Regularization - Theory.srt 5.9 KB
4. Practical machine learning issues/11. Gradient Descent Tutorial.srt 5.9 KB
4. Practical machine learning issues/10. The Dummy Variable Trap.srt 5.9 KB
2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.srt 5.9 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
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.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/5. Polynomial regression - extending linear regression (with Python code).srt 5.3 KB
2. 1-D Linear Regression Theory and Code/11. Suggestion Box.srt 4.9 KB
4. Practical machine learning issues/5. Categorical inputs.srt 4.8 KB
1. Welcome/1. Welcome.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
4. Practical machine learning issues/2. Interpreting the Weights.srt 4.5 KB
4. Practical machine learning issues/14. L1 Regularization - Theory.srt 4.5 KB
4. Practical machine learning issues/16. L1 vs L2 Regularization.srt 4.5 KB
9. Appendix FAQ Finale/1. What is the Appendix.srt 3.9 KB
4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.srt 3.9 KB
4. Practical machine learning issues/15. L1 Regularization - Code.srt 3.8 KB
2. 1-D Linear Regression Theory and Code/7. Introduction to Moore's Law Problem.srt 3.7 KB
4. Practical machine learning issues/9. L2 Regularization - Code.srt 3.6 KB
4. Practical machine learning issues/12. Gradient Descent for Linear Regression.srt 3.4 KB
3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.srt 2.9 KB
4. Practical machine learning issues/3. Generalization error, train and test sets.srt 2.8 KB
4. Practical machine learning issues/6. One-Hot Encoding Quiz.srt 2.7 KB
2. 1-D Linear Regression Theory and Code/10. R-squared Quiz 1.srt 2.4 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 1.8 KB
2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.srt 1.6 KB
1. Welcome/6. What can linear regression be used for.html 150 bytes

Udemy - Deep Learning Prerequisites Linear Regression in Python (1.2021)

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