Deep Learning Prerequisites: Linear Regression in Python
What you will 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
Curriculum
Section 1: Welcome
Section 2: 1D Linear Regression: Theory and Code
Section 3: Multiple linear regression and polynomial regression
Section 4: Practical machine learning issues
Section 5: Conclusion and Next Steps
Section 6: Appendix
Course Description
Data science: Learn linear regression from scratch and build your own working program in Python for data analysis.
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 realworld 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 1D 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 1D linear regression to anydimensional linear regression  in other words, how to create a machine learning model that can learn from multiple inputs.
We will apply multidimensional 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, traintest 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
 Selftaught programmers who want to improve their computer science theoretical skills
 Analytics experts who want to learn the theoretical basis behind one of statistics' mostused algorithms