FreeCourseLab com Udemy - Deep Learning Prerequisites Linear Regression in Python

1. 1. Welcome/1. Welcome.mp449.68MB
2. 1. Welcome/2. Introduction and Outline.mp46.33MB
3. 1. Welcome/3. What is machine learning How does linear regression play a role.mp48.44MB
4. 1. Welcome/4. Introduction to Moore's Law Problem.mp44.42MB
5. 1. Welcome/6. How to Succeed in this Course.mp43.31MB
6. 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp419.34MB
7. 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp424.67MB
8. 2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.mp414.44MB
9. 2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.mp41.05MB
10. 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp411.31MB
11. 2. 1-D Linear Regression Theory and Code/6. R-squared in code.mp44.5MB
12. 2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.mp417.5MB
13. 2. 1-D Linear Regression Theory and Code/8. R-squared Quiz 1.mp42.8MB
14. 3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).mp414.43MB
15. 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp436.08MB
16. 3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.mp43.1MB
17. 3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp414.91MB
18. 3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).mp416.4MB
19. 3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.mp412.35MB
20. 3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.mp43.5MB
21. 4. Practical machine learning issues/1. What do all these letters mean.mp49.63MB
22. 4. Practical machine learning issues/10. The Dummy Variable Trap.mp46.08MB
23. 4. Practical machine learning issues/11. Gradient Descent Tutorial.mp422.8MB
24. 4. Practical machine learning issues/12. Gradient Descent for Linear Regression.mp43.5MB
25. 4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.mp48.51MB
26. 4. Practical machine learning issues/14. L1 Regularization - Theory.mp44.66MB
27. 4. Practical machine learning issues/15. L1 Regularization - Code.mp48.27MB
28. 4. Practical machine learning issues/16. L1 vs L2 Regularization.mp44.8MB
29. 4. Practical machine learning issues/2. Interpreting the Weights.mp46.05MB
30. 4. Practical machine learning issues/3. Generalization error, train and test sets.mp44.39MB
31. 4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp417.26MB
32. 4. Practical machine learning issues/5. Categorical inputs.mp48.19MB
33. 4. Practical machine learning issues/6. One-Hot Encoding Quiz.mp43.77MB
34. 4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.mp48.14MB
35. 4. Practical machine learning issues/8. L2 Regularization - Theory.mp46.66MB
36. 4. Practical machine learning issues/9. L2 Regularization - Code.mp48.09MB
37. 5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.mp48.13MB
38. 5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.mp47.17MB
39. 6. Appendix/1. What is the Appendix.mp45.46MB
40. 6. Appendix/10. What order should I take your courses in (part 1).mp429.32MB
41. 6. Appendix/11. What order should I take your courses in (part 2).mp437.62MB
42. 6. Appendix/12. Python 2 vs Python 3.mp47.84MB
43. 6. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp44.03MB
44. 6. Appendix/3. Windows-Focused Environment Setup 2018.mp4186.29MB
45. 6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
46. 6. Appendix/5. How to Code by Yourself (part 1).mp424.54MB
47. 6. Appendix/6. How to Code by Yourself (part 2).mp414.81MB
48. 6. Appendix/7. How to Succeed in this Course (Long Version).mp418.32MB
49. 6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.96MB
50. 6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp478.29MB