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Deep Learning Prerequisites: Linear Regression in Python

Name: Deep Learning Prerequisites: Linear Regression in Python
Rating: 4.6 (6815 reviews)

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

Created byLazy Programmer Inc., Lazy Programmer Team

Last updated 2/2026

English

English [Auto],

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

Course content

10 sections • 54 lectures • 6h 21m total length

Introduction and Outline7:41
How to Succeed in this Course3:04
Statistics vs. Machine Learning9:58
Explore the difference between machine learning and statistics using linear and logistic regression as tools; learn loss functions, training and testing sets, generalization, overfitting, regularization, model diagnostics, and interpretation.

What is machine learning? How does linear regression play a role?5:13
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.
Define the model in 1-D, derive the solution (Updated Version)12:43
Define the model in 1-D, derive the solution14:52
Derive the one-dimensional linear regression model y_hat = a x + b, minimize squared error with calculus, and solve the two equations for a and b in Python.
Coding the 1-D solution in Python7:38
Code the 1-D linear regression solution in Python from scratch, using numpy to compute slope and intercept, and visualize with matplotlib by plotting data and the line.
Exercise: Theory vs. Code1:19
Determine how good the model is - r-squared5:50
R-squared in code2:15
Introduction to Moore's Law Problem2:30
Apply linear regression in Python to model Moore's law by plotting year versus the log transistor counts, revealing a linear relationship and the doubling every two years.
Demonstrating Moore's Law in Code8:00
Moore's Law Derivation6:02
R-squared Quiz 11:48
Suggestion Box3:10

Define the multi-dimensional problem and derive the solution (Updated Version)9:34
Define the multi-dimensional problem and derive the solution17:07
How to solve multiple linear regression using only matrices1:55
Coding the multi-dimensional solution in Python7:29
Polynomial regression - extending linear regression (with Python code)7:56
Predicting Systolic Blood Pressure from Age and Weight5:45
R-squared Quiz 22:05
Add a random noise column to X in a multiple linear regression and observe how R-squared can improve slightly due to sampling error, even when correlation is zero.

What do all these letters mean?6:23
Clarify linear regression notation, mapping inputs X to targets Y or T. Identify predictions y hat, weights W, and key terms such as error, cost, objective, and log likelihood.
Interpreting the Weights4:00
Generalization error, train and test sets2:49
Generalization and Overfitting Demonstration in Code7:32
Categorical inputs5:21
One-Hot Encoding Quiz2:07
Compute one-hot encoding dimensionality for two inputs: news mentions (two categories) and sentiment (happy, angry, sad). The model's dimensionality equals five, with possible modifications discussed.
Probabilistic Interpretation of Squared Error5:15
L2 Regularization - Theory4:21
L2 Regularization - Code4:13
The Dummy Variable Trap3:58
Gradient Descent Tutorial4:30
Gradient Descent for Linear Regression2:13
Bypass the Dummy Variable Trap with Gradient Descent4:17
L1 Regularization - Theory3:05
Explore L1 regularization for linear regression to achieve sparsity by using a Laplace prior on weights. Apply gradient descent to optimize the L1-penalized cost and manage high dimensional data.
L1 Regularization - Code4:25
L1 vs L2 Regularization3:05
Why Divide by Square Root of D?6:32

Brief overview of advanced linear regression and machine learning topics5:14
Discover how to improve linear regression in Python with cross-validation, standardization, and regularisation, and explore related supervised and unsupervised learning algorithms for a stronger data science foundation.
Exercises, practice, and how to get good at this3:54
Practice data science with linear regression in Python by using real datasets, splitting into train and test sets, and evaluating models with R-squared and variable selection.

How to Succeed in this Course (Long Version)10:24
Ask questions in the Q&A, meet course prerequisites, and implement theory in code to bridge gaps from background knowledge to deep learning concepts.
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?22:04
Explore how deep learning prerequisites balance academic math with practical coding, and implement linear regression in Python on real-world data like text and images and two-dimensional datasets.
Machine Learning and AI Prerequisite Roadmap (pt 1)11:18
Learn a skill-based roadmap to mastering machine learning, from linear and logistic regression to neural networks, reinforcement learning, and deep learning with Python, highlighting dependencies and practical paths.
Machine Learning and AI Prerequisite Roadmap (pt 2)16:07

Requirements

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

Description

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 want more than 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

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ 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

Deep Learning Prerequisites: Linear Regression in Python

What you'll learn

Explore related topics

Course content

Welcome3 lectures • 21min

1-D Linear Regression: Theory and Code12 lectures • 1hr 11min

Multiple linear regression and polynomial regression7 lectures • 52min

Practical machine learning issues17 lectures • 1hr 14min

Conclusion and Next Steps2 lectures • 9min

Appendix / FAQ Intro1 lecture • 4min

Setting Up Your Environment (FAQ by Student Request)3 lectures • 42min

Extra Help With Python Coding for Beginners (FAQ by Student Request)4 lectures • 42min

Effective Learning Strategies for Machine Learning (FAQ by Student Request)4 lectures • 1hr

Appendix / FAQ Finale1 lecture • 6min

Requirements

Description

Who this course is for: