# Deep Learning Prerequisites: Linear Regression in Python

4.6 (4458)
MOOC
Learning paid
English
6.5 hours course
Course by Udemy
\$ 96.99
\$ 96.99
What will you 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

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.

Suggested Prerequisites:

• calculus (taking derivatives)
• matrix arithmetic
• probability
• Python coding: if/else, loops, lists, dicts, sets

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)

Program
Welcome
Understand what linear regression is and how we will apply it
Welcome
Introduction and Outline
What is machine learning? How does linear regression play a role?
We will discuss a broad outline of what machine learning is, and how linear regression fits into the ecosystem of machine learning. We will discuss some examples of linear regression to give you a feel for what it can be used for.
Introduction to Moore's Law Problem
What can linear regression be used for?
How to Succeed in this Course
1-D Linear Regression: Theory and Code
How to code a simple linear regression model in Python and measure its performance.
Define the model in 1-D, derive the solution (Updated Version)
Define the model in 1-D, derive the solution
Coding the 1-D solution in Python
Exercise: Theory vs. Code
Requirements
• How to take a derivative using calculus
• Basic Python programming
• For the advanced section of the course, you will need to know probability
Lecturers
Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Platform
Udemy courses are suited to professional development. The platform is organized in such a way that it is experts themselves that decide the topic and when the course will start. All supporting documents are made available to you for lifetime access. On this platform, you can find a course on about any subject, and that is no exaggeration – from a tutorial on how to ride a motorcycle, to managing the financial markets. The language and the course format are established by the teacher. This is why it is important to read the information about the course carefully before parting with any money.
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