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Udemy | Deep Learning Prerequisites: Linear Regression in Python [FTU] TGx Exclusive

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English

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999.52 MB

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b62ba45397ca5d56ca69de0d05850a70f673e212

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Prom3th3uS :_verified_uploader:

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23-05-2019 17:28

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File	Size
0. Websites you may like/1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url	328.00 B
0. Websites you may like/2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url	286.00 B
0. Websites you may like/3. (NulledPremium.com) Download Cracked Website Themes, Plugins, Scripts And Stock Images.url	163.00 B
0. Websites you may like/4. (FTUApps.com) Download Cracked Developers Applications For Free.url	239.00 B
0. Websites you may like/5. (Discuss.FTUForum.com) FTU Discussion Forum.url	294.00 B
0. Websites you may like/How you can help Team-FTU.txt	237.00 B
1. Welcome/1. Welcome.mp4	32.00 MB
1. Welcome/1. Welcome.vtt	4.00 kB
1. Welcome/2. Introduction and Outline.mp4	28.40 MB
1. Welcome/2. Introduction and Outline.vtt	5.30 kB
1. Welcome/3. What is machine learning How does linear regression play a role.mp4	8.44 MB
1. Welcome/3. What is machine learning How does linear regression play a role.vtt	5.28 kB
1. Welcome/4. Introduction to Moore's Law Problem.mp4	4.42 MB
1. Welcome/4. Introduction to Moore's Law Problem.vtt	3.39 kB
1. Welcome/5. What can linear regression be used for.html	143.00 B
1. Welcome/6. How to Succeed in this Course.mp4	3.31 MB
1. Welcome/6. How to Succeed in this Course.vtt	3.49 kB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4	19.34 MB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).vtt	14.36 kB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4	24.67 MB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.vtt	9.58 kB
2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.mp4	14.44 MB
2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.vtt	4.86 kB
2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.mp4	1.05 MB
2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.vtt	1.38 kB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4	11.31 MB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.vtt	4.15 kB
2. 1-D Linear Regression Theory and Code/6. R-squared in code.mp4	4.50 MB
2. 1-D Linear Regression Theory and Code/6. R-squared in code.vtt	1.50 kB
2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.mp4	17.50 MB
2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.vtt	6.17 kB
2. 1-D Linear Regression Theory and Code/8. R-squared Quiz 1.mp4	2.80 MB
2. 1-D Linear Regression Theory and Code/8. R-squared Quiz 1.vtt	1.96 kB
3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).mp4	14.43 MB
3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).vtt	10.28 kB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4	36.08 MB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.vtt	11.37 kB
3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.mp4	3.10 MB
3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.vtt	1.84 kB
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp4	14.91 MB
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.vtt	4.51 kB
3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).mp4	16.40 MB
3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).vtt	4.26 kB
3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.mp4	12.35 MB
3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.vtt	4.88 kB
3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.mp4	3.50 MB
3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.vtt	2.38 kB
4. Practical machine learning issues/1. What do all these letters mean.mp4	9.63 MB
4. Practical machine learning issues/1. What do all these letters mean.vtt	7.01 kB
4. Practical machine learning issues/10. The Dummy Variable Trap.mp4	6.08 MB
4. Practical machine learning issues/10. The Dummy Variable Trap.vtt	4.85 kB
4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4	22.80 MB
4. Practical machine learning issues/11. Gradient Descent Tutorial.vtt	4.80 kB
4. Practical machine learning issues/12. Gradient Descent for Linear Regression.mp4	3.50 MB
4. Practical machine learning issues/12. Gradient Descent for Linear Regression.vtt	2.76 kB
4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.mp4	8.51 MB
4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.vtt	3.08 kB
4. Practical machine learning issues/14. L1 Regularization - Theory.mp4	4.66 MB
4. Practical machine learning issues/14. L1 Regularization - Theory.vtt	3.65 kB
4. Practical machine learning issues/15. L1 Regularization - Code.mp4	8.27 MB
4. Practical machine learning issues/15. L1 Regularization - Code.vtt	3.06 kB
4. Practical machine learning issues/16. L1 vs L2 Regularization.mp4	4.80 MB
4. Practical machine learning issues/16. L1 vs L2 Regularization.vtt	3.75 kB
4. Practical machine learning issues/2. Interpreting the Weights.mp4	6.05 MB
4. Practical machine learning issues/2. Interpreting the Weights.vtt	3.72 kB
4. Practical machine learning issues/3. Generalization error, train and test sets.mp4	4.39 MB
4. Practical machine learning issues/3. Generalization error, train and test sets.vtt	2.58 kB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp4	17.26 MB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.vtt	8.21 kB
4. Practical machine learning issues/5. Categorical inputs.mp4	8.19 MB
4. Practical machine learning issues/5. Categorical inputs.vtt	4.34 kB
4. Practical machine learning issues/6. One-Hot Encoding Quiz.mp4	3.77 MB
4. Practical machine learning issues/6. One-Hot Encoding Quiz.vtt	2.20 kB
4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.mp4	8.14 MB
4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.vtt	5.66 kB
4. Practical machine learning issues/8. L2 Regularization - Theory.mp4	6.66 MB
4. Practical machine learning issues/8. L2 Regularization - Theory.vtt	4.84 kB
4. Practical machine learning issues/9. L2 Regularization - Code.mp4	8.09 MB
4. Practical machine learning issues/9. L2 Regularization - Code.vtt	2.96 kB
5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.mp4	8.13 MB
5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.vtt	5.13 kB
5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.mp4	7.17 MB
5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.vtt	4.83 kB
6. Appendix/1. What is the Appendix.mp4	5.46 MB
6. Appendix/1. What is the Appendix.vtt	3.28 kB
6. Appendix/10. What order should I take your courses in (part 1).mp4	29.32 MB
6. Appendix/10. What order should I take your courses in (part 1).vtt	14.09 kB
6. Appendix/11. What order should I take your courses in (part 2).mp4	81.67 MB
6. Appendix/11. What order should I take your courses in (part 2).vtt	37.65 MB
6. Appendix/12. Python 2 vs Python 3.mp4	16.89 MB
6. Appendix/12. Python 2 vs Python 3.vtt	5.35 kB
6. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4	4.03 MB
6. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.vtt	2.99 kB
6. Appendix/3. Windows-Focused Environment Setup 2018.mp4	186.29 MB
6. Appendix/3. Windows-Focused Environment Setup 2018.vtt	17.39 kB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4	43.92 MB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt	12.40 kB
6. Appendix/5. How to Code by Yourself (part 1).mp4	24.54 MB
6. Appendix/5. How to Code by Yourself (part 1).vtt	19.78 kB
6. Appendix/6. How to Code by Yourself (part 2).mp4	14.81 MB
6. Appendix/6. How to Code by Yourself (part 2).vtt	11.62 kB
6. Appendix/7. How to Succeed in this Course (Long Version).mp4	18.32 MB
6. Appendix/7. How to Succeed in this Course (Long Version).vtt	12.79 kB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4	38.96 MB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt	27.77 kB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4	78.29 MB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt	12.22 kB

Description

Data science: Learn linear regression from scratch and build your own working program in Python for data analysis.

BESTSELLER

Created by: Lazy Programmer Inc.
Last updated: 3/2019
Language: English
Torrent Contains: 107 Files, 7 Folders
Course Source: https://www.udemy.com/data-science-linear-regression-in-python/

What you'll learn

• Derive and solve a linear regression model, and apply it appropriately to data science problems
• Program your own version of a linear regression model in Python

Requirements

• How to take a derivative using calculus
• Basic Python programming
• For the advanced section of the course, you will need to know probability
• For the advanced section of the course, you will need to know the Gaussian distribution

Description

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

In the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true.

What's that you say? Moore's Law is not linear?

You are correct! I will show you how linear regression can still be applied.

In the next section, we will extend 1-D linear regression to any-dimensional linear regression - in other words, how to create a machine learning model that can learn from multiple inputs.

We will apply multi-dimensional linear regression to predicting a patient's systolic blood pressure given their age and weight.

Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, train-test splits, and so on.

This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for FREE.

If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want to know how to apply your skills as a software engineer or "hacker", this course may be useful.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

• calculus
• linear algebra
• probability
• Python coding: if/else, loops, lists, dicts, sets
• Numpy coding: matrix and vector operations, loading a CSV file

TIPS (for getting through the course):

• Watch it at 2x.
• Take handwritten notes. This will drastically increase your ability to retain the information.
• Write down the equations. If you don't, I guarantee it will just look like gibberish.
• Ask lots of questions on the discussion board. The more the better!
• Realize that most exercises will take you days or weeks to complete.
• Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

• Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

• People who are interested in data science, machine learning, statistics and artificial intelligence
• People new to data science who would like an easy introduction to the topic
• People who wish to advance their career by getting into one of technology's trending fields, data science
• Self-taught programmers who want to improve their computer science theoretical skills
• Analytics experts who want to learn the theoretical basis behind one of statistics' most-used algorithms.

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Our Forum for discussion >>> https://discuss.ftuforum.com/