[FreeTutorials.Us] data-science-linear-regression-in-python
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- 06 Appendix/035 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp4 43.9 MB
- 03 Multiple linear regression and polynomial regression/012 Define the multi-dimensional problem and derive the solution.mp4 36.1 MB
- 02 1-D Linear Regression Theory and Code/006 Define the model in 1-D derive the solution.mp4 24.7 MB
- 06 Appendix/036 How to Code by Yourself part 1.mp4 24.5 MB
- 02 1-D Linear Regression Theory and Code/005 Define the model in 1-D derive the solution Updated Version.mp4 19.3 MB
- 02 1-D Linear Regression Theory and Code/010 Demonstrating Moores Law in Code.mp4 17.5 MB
- 04 Practical machine learning issues/020 Generalization and Overfitting Demonstration in Code.mp4 17.2 MB
- 03 Multiple linear regression and polynomial regression/015 Polynomial regression - extending linear regression with Python code.mp4 16.4 MB
- 03 Multiple linear regression and polynomial regression/014 Coding the multi-dimensional solution in Python.mp4 14.9 MB
- 06 Appendix/037 How to Code by Yourself part 2.mp4 14.8 MB
- 03 Multiple linear regression and polynomial regression/011 Define the multi-dimensional problem and derive the solution Updated Version.mp4 14.4 MB
- 02 1-D Linear Regression Theory and Code/007 Coding the 1-D solution in Python.mp4 14.4 MB
- 03 Multiple linear regression and polynomial regression/016 Predicting Systolic Blood Pressure from Age and Weight.mp4 12.3 MB
- 02 1-D Linear Regression Theory and Code/008 Determine how good the model is - r-squared.mp4 11.3 MB
- 04 Practical machine learning issues/017 What do all these letters mean.mp4 9.6 MB
- 01 Introduction and Outline/004 How to Succeed in this Course.mp4 8.8 MB
- 04 Practical machine learning issues/028 Bypass the Dummy Variable Trap with Gradient Descent.mp4 8.5 MB
- 01 Introduction and Outline/002 What is machine learning How does linear regression play a role.mp4 8.4 MB
- 04 Practical machine learning issues/030 L1 Regularization - Code.mp4 8.3 MB
- 04 Practical machine learning issues/021 Categorical inputs.mp4 8.2 MB
- 05 Conclusion and Next Steps/032 Brief overview of advanced linear regression and machine learning topics.mp4 8.1 MB
- 04 Practical machine learning issues/022 Probabilistic Interpretation of Squared Error.mp4 8.1 MB
- 04 Practical machine learning issues/024 L2 Regularization - Code.mp4 8.1 MB
- 04 Practical machine learning issues/026 Gradient Descent Tutorial.mp4 7.7 MB
- 05 Conclusion and Next Steps/033 Exercises practice and how to get good at this.mp4 7.2 MB
- 04 Practical machine learning issues/023 L2 Regularization - Theory.mp4 6.6 MB
- 01 Introduction and Outline/001 Introduction and Outline.mp4 6.3 MB
- 04 Practical machine learning issues/025 The Dummy Variable Trap.mp4 6.1 MB
- 04 Practical machine learning issues/018 Interpreting the Weights.mp4 6.0 MB
- 04 Practical machine learning issues/031 L1 vs L2 Regularization.mp4 4.8 MB
- 04 Practical machine learning issues/029 L1 Regularization - Theory.mp4 4.7 MB
- 02 1-D Linear Regression Theory and Code/009 R-squared in code.mp4 4.5 MB
- 01 Introduction and Outline/003 Introduction to Moores Law Problem.mp4 4.4 MB
- 04 Practical machine learning issues/019 Generalization error train and test sets.mp4 4.4 MB
- 06 Appendix/034 BONUS Where to get Udemy coupons and FREE deep learning material.mp4 4.0 MB
- 04 Practical machine learning issues/027 Gradient Descent for Linear Regression.mp4 3.5 MB
- 03 Multiple linear regression and polynomial regression/013 How to solve multiple linear regression using only matrices.mp4 3.1 MB
- 04 Practical machine learning issues/quizzes/004 One-hot encoding.html 2.9 KB
- 03 Multiple linear regression and polynomial regression/quizzes/003 R-squared.html 2.8 KB
- 01 Introduction and Outline/quizzes/001 What can linear regression be used for.html 2.5 KB
- 02 1-D Linear Regression Theory and Code/quizzes/002 R-squared.html 2.4 KB
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