Deep Learning Prerequisites: Linear Regression in Python

Data science, machine learning, and artificial intelligence in Python for students and professionals

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
• Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion
• Understand regularization for machine learning and deep learning
• Understand closed-form solutions vs. numerical methods like gradient descent
• Apply linear regression to a wide variety of real-world problems

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.

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 wantmorethan just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

• Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

• My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

• Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

• After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

• calculus (taking derivatives)

• matrix arithmetic

• probability

• Python coding: if/else, loops, lists, dicts, sets

• Numpy coding: matrix and vector operations, loading a CSV file

WHATORDERSHOULDITAKEYOURCOURSESIN?:

• Check out the lecture "Machine Learning and AIPrerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)