Udemy - Deep Learning Prerequisites: Linear Regression in Python

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.1 GB
  • Uploaded Bytutplanet
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  • Last checkedNov. 10th '21
  • Date uploadedNov. 07th '21
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Udemy - Deep Learning Prerequisites: Linear Regression in Python

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python.

Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:

deep learning
machine learning
data science
statistics
For more Udemy Courses: https://tutorialsplanet.net

Files:

[Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Linear Regression in Python 1. Welcome
  • 1. Introduction and Outline.mp4 (42.7 MB)
  • 1. Introduction and Outline.srt (10.8 KB)
  • 2. How to Succeed in this Course.mp4 (43.8 MB)
  • 2. How to Succeed in this Course.srt (8.3 KB)
  • 3. Statistics vs. Machine Learning.mp4 (55.5 MB)
  • 3. Statistics vs. Machine Learning.srt (14.7 KB)
  • [Tutorialsplanet.NET].url (0.1 KB)
2. 1-D Linear Regression Theory and Code
  • 1. What is machine learning How does linear regression play a role.mp4 (8.4 MB)
  • 1. What is machine learning How does linear regression play a role.srt (5.8 KB)
  • 10. Demonstrating Moore's Law in Code.mp4 (17.5 MB)
  • 10. Demonstrating Moore's Law in Code.srt (6.9 KB)
  • 11. Moore's Law Derivation.mp4 (20.2 MB)
  • 11. Moore's Law Derivation.srt (7.6 KB)
  • 12. R-squared Quiz 1.mp4 (2.8 MB)
  • 12. R-squared Quiz 1.srt (2.2 KB)
  • 13. Suggestion Box.mp4 (16.1 MB)
  • 13. Suggestion Box.srt (4.7 KB)
  • 2. What can linear regression be used for.html (0.1 KB)
  • 3. Define the model in 1-D, derive the solution (Updated Version).mp4 (19.4 MB)
  • 3. Define the model in 1-D, derive the solution (Updated Version).srt (16.5 KB)
  • 4. Define the model in 1-D, derive the solution.mp4 (24.7 MB)
  • 4. Define the model in 1-D, derive the solution.srt (11.1 KB)
  • 5. Coding the 1-D solution in Python.mp4 (14.4 MB)
  • 5. Coding the 1-D solution in Python.srt (5.6 KB)
  • 6. Exercise Theory vs. Code.mp4 (1.0 MB)
  • 6. Exercise Theory vs. Code.srt (1.6 KB)
  • 7. Determine how good the model is - r-squared.mp4 (11.3 MB)
  • 7. Determine how good the model is - r-squared.srt (4.7 KB)
  • 8. R-squared in code.mp4 (4.5 MB)
  • 8. R-squared in code.srt (1.7 KB)
  • 9. Introduction to Moore's Law Problem.mp4 (4.4 MB)
  • 9. Introduction to Moore's Law Problem.srt (3.7 KB)
3. Multiple linear regression and polynomial regression
  • 1. Define the multi-dimensional problem and derive the solution (Updated Version).mp4 (14.4 MB)
  • 1. Define the multi-dimensional problem and derive the solution (Updated Version).srt (11.7 KB)
  • 2. Define the multi-dimensional problem and derive the solution.mp4 (36.1 MB)
  • 2. Define the multi-dimensional problem and derive the solution.srt (12.9 KB)
  • 3. How to solve multiple linear regression using only matrices.mp4 (3.1 MB)
  • 3. How to solve multiple linear regression using only matrices.srt (2.0 KB)
  • 4. Coding the multi-dimensional solution in Python.mp4 (14.9 MB)
  • 4. Coding the multi-dimensional solution in Python.srt (5.2 KB)
  • 5. Polynomial regression - extending linear regression (with Python code).mp4 (16.4 MB)
  • 5. Polynomial regression - extending linear regression (with Python code).srt (4.9 KB)
  • 6. Predicting Systolic Blood Pressure from Age and Weight.mp4 (12.3 MB)
  • 6. Predicting Systolic Blood Pressure from Age and Weight.srt (5.5 KB)
  • 7. R-squared Quiz 2.mp4 (3.5 MB)
  • 7. R-squared Quiz 2.srt (2.7 KB)
4. Practical machine learning issues
  • 1. What do all these letters mean.mp4 (9.6 MB)
  • 1. What do all these letters mean.srt (8.0 KB)
  • 10. The Dummy Variable Trap.mp4 (6.1 MB)
  • 10. The Dummy Variable Trap.srt (5.5 KB)
  • 11. Gradient Descent Tutorial.mp4 (22.8 MB)
  • 11. Gradient Descent Tutorial.srt (5.5 KB)
  • 12. Gradient Descent for Linear Regression.mp4 (3.5 MB)
  • 12. Gradient Descent for Linear Regression.srt (3.1 KB)
  • 13. Bypass the Dummy Variable Trap with Gradient Descent.mp4 (8.5 MB)
  • 13. Bypass the Dummy Variable Trap with Gradient Descent.srt (3.6 KB)
  • 14. L1 Regularization - Theory.mp4 (4.7 MB)
  • 14. L1 Regularization - Theory.srt (4.1 KB)
  • 15. L1 Regularization - Code.mp4 (8.3 MB)
  • 15. L1 Regularization - Code.srt (3.5 KB)
  • 16. L1 vs L2 Regularization.mp4 (4.8 MB)
  • 16. L1 vs L2 Regularization.srt (4.3 KB)
  • 17. Why Divide by Square Root of D.mp4 (23.5 MB)
  • 17. Why Divide by Square Root of D.srt (8.7 KB)
  • 2. Interpreting the Weights.mp4 (14.2 MB)
  • 2. Interpreting the Weights.srt (4.3 KB)
  • 3. Generalization error, train and test sets.mp4 (4.4 MB)
  • 3. Generalization error, train and test sets.srt (2.8 KB)
  • 4. Generalization and Overfitting Demonstration in Code.mp4 (17.2 MB)
  • 4. Generalization and Overfitting Demonstration in Code.srt (9.2 KB)
  • 5. Categorical inputs.mp4 (8.2 MB)
  • 5. Categorical inputs.srt (4.8 KB)
  • 6. One-Hot Encoding Quiz.mp4 (3.8 MB)
  • 6. One-Hot Encoding Quiz.srt (2.5 KB)
  • 7. Probabilistic Interpretation of Squared Error.mp4 (8.1 MB)
  • 7. Probabilistic Interpretation of Squared Error.srt (6.4 KB)
  • 8. L2 Regularization - Theory.mp4 (6.7 MB)
  • 8. L2 Regularization - Theory.srt (5.5 KB)
  • 9. L2 Regularization - Code.mp4 (8.1 MB)
  • 9. L2 Regularization - Code.srt (3.4 KB)
5. Conclusion and Next Steps
  • 1. Brief overview of advanced linear regression and machine learning topics.mp4 (8.1 MB)
  • 1. Brief overview of advanced linear regression and machine learning topics.srt (5.7 KB)
  • 2. Exercises, practice, and how to get good at this.mp4 (7.2 MB)
  • 2. Exercises, practice, and how to get good at this.srt (5.3 KB)
6. Setting Up Your Environment (FAQ by Student Request)
  • 1. Anaconda Environment Setup.mp4 (186.3 MB)
  • 1. Anaconda Environment Setup.srt (20.1 KB)
  • 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (43.9 MB)
  • 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt (14.5 KB)
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)
  • 1. How to Code by Yourself (part 1).mp4 (24.5 MB)
  • 1. How to Code by Yourself (part 1).srt (22.8 KB)
  • 2. How to Code by Yourself (part 2).mp4 (14.8 MB)
  • 2. How to Code by Yourself (part 2).srt (13.3 KB)
  • 3. Proof that using Jupyter Notebook is the same as not using it.mp4 (78.3 MB)
  • 3. Proof that using Jupyter Notebook is the same as not using it.srt (14.1 KB)

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