Deep Learning Prerequisites: Linear Regression in Python from Udemy

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)

What's inside

Learning objectives

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

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

Syllabus

Understand what linear regression is and how we will apply it

Introduction and Outline

How to Succeed in this Course

Statistics vs. Machine Learning

How to code a simple linear regression model in Python and measure its performance.

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.

What can linear regression be used for?

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

Determine how good the model is - r-squared

R-squared in code

Introduction to Moore's Law Problem

Demonstrating Moore's Law in Code

Moore's Law Derivation

R-squared Quiz 1

Suggestion Box

Solving and coding linear regression in multiple dimensions, and applying linear regression to polynomials.

Define the multi-dimensional problem and derive the solution (Updated Version)

Define the multi-dimensional problem and derive the solution

How to solve multiple linear regression using only matrices

Coding the multi-dimensional solution in Python

Polynomial regression - extending linear regression (with Python code)

Predicting Systolic Blood Pressure from Age and Weight

R-squared Quiz 2

Understand generalization error, overfitting, cross-validation, and how to use different data-types (categorical, real-valued) as input into a linear regression model.

What do all these letters mean?

Interpreting the Weights

Generalization error, train and test sets

Generalization and Overfitting Demonstration in Code

Categorical inputs

One-Hot Encoding Quiz

Probabilistic Interpretation of Squared Error

L2 Regularization - Theory

L2 Regularization - Code

The Dummy Variable Trap

Gradient Descent Tutorial

Gradient Descent for Linear Regression

Bypass the Dummy Variable Trap with Gradient Descent

L1 Regularization - Theory

L1 Regularization - Code

L1 vs L2 Regularization

Why Divide by Square Root of D?

Conclusion and Next Steps

Brief overview of advanced linear regression and machine learning topics

Exercises, practice, and how to get good at this

Setting Up Your Environment (FAQ by Student Request)

Pre-Installation Check

Anaconda Environment Setup

How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners (FAQ by Student Request)

How to Code by Yourself (part 1)

How to Code by Yourself (part 2)

Proof that using Jupyter Notebook is the same as not using it

Python 2 vs Python 3

Effective Learning Strategies for Machine Learning (FAQ by Student Request)

How to Succeed in this Course (Long Version)

Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Machine Learning and AI Prerequisite Roadmap (pt 1)

Machine Learning and AI Prerequisite Roadmap (pt 2)

Misc. topics tangentially related to the course that may help you with the course materials

What is the Appendix?

BONUS

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Focuses on hands-on coding to build an understanding of linear regression

Emphasizes practical applications of linear regression to real-world problems

Provides a solid foundation for beginners in machine learning and data science

Covers advanced concepts such as regularization and gradient descent, making it suitable for intermediate learners as well

Instructed by an experienced professional in the field, who provides valuable insights

Course materials include Python coding tutorials, making it easy for learners to apply concepts

Reviews summary

Introductory linear regression in python

Learners say that this introductory course in linear regression is well received with good implementation of concepts from the ground up in code. The assumptions of linear regression, however, are slightly glossed over.

Implementation of concepts in code.

"A very good introduction to linear regression."

"The implementation of all the concepts from the ground up in code is an excellent way to drive home the concepts."

Assumptions not covered in-depth.

"My only complaint/wish would be to have a small section addressing the assumptions of linear regression."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deep Learning Prerequisites: Linear Regression in Python with these activities:

Practice Matrix Algebra

Show steps

Strengthen your foundation in matrix algebra, which is essential for understanding linear regression.

Browse courses on Matrix Algebra

Show steps

Review the concepts of matrix addition, multiplication, and inversion.
Solve practice problems involving matrix operations.
Apply matrix algebra to real-world examples, such as solving systems of equations.

Review 'Introduction to Statistical Learning'

Show steps

Supplement your understanding of linear regression by reading a foundational text on statistical learning.

View An Introduction to Statistical Learning: with... on Amazon

Show steps

Read the introductory chapter to gain an overview of the book's content.
Focus on the chapters covering linear regression and its applications.
Take notes and highlight important concepts.

Join a Study Group for Linear Regression

Show steps

Engage with peers in a study group to discuss concepts, exchange ideas, and reinforce your understanding of linear regression.

Browse courses on Linear Regression

Show steps

Find or create a study group with fellow students.
Meet regularly to discuss course materials, assignments, and practice problems.
Collaborate on projects and presentations to strengthen your knowledge.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Connect with an Expert in Linear Regression

Show steps

Seek guidance from an expert in linear regression to gain insights and accelerate your learning.

Browse courses on Linear Regression

Show steps

Identify potential mentors through online platforms, conferences, or personal connections.
Reach out to mentors and express your interest in connecting.
Schedule regular meetings to discuss your progress and seek advice.
Leverage your mentor's knowledge and experience to enhance your understanding.

Follow a Tutorial on Regularization Techniques

Show steps

Enhance your understanding of regularization techniques commonly used in linear regression and machine learning.

Browse courses on Regularization

Show steps

Identify a reputable online course or tutorial on regularization techniques.
Follow the tutorial and take notes on the different methods.
Implement the techniques in Python or another programming language.

Practice Linear Regression Python Coding Exercise

Show steps

Practice coding a linear regression model in Python by following along with a guided exercise.

Browse courses on Linear Regression

Show steps

Set up your Python environment and install the necessary libraries.
Load a dataset and prepare the data for modeling.
Define the linear regression model and fit it to the data.
Evaluate the performance of the model.

Create a Visual Representation of Linear Regression

Show steps

Create a visual representation of linear regression to help you understand the concept more deeply.

Browse courses on Linear Regression

Show steps

Choose a dataset and prepare the data for visualization.
Select a visualization tool and create a scatter plot of the data.
Fit a linear regression model to the data and plot the line of best fit.
Analyze the visualization and identify insights about the relationship between the variables.

Build a Linear Regression Model for a Real-World Problem

Show steps

Apply your knowledge of linear regression to solve a real-world problem and create a tangible deliverable.

Browse courses on Linear Regression

Show steps

Identify a problem that can be addressed using linear regression.
Collect and prepare the necessary data.
Build and train a linear regression model using Python or another programming language.
Evaluate the performance of the model and make adjustments as needed.
Create a report or presentation summarizing your findings.

Career center

Learners who complete Deep Learning Prerequisites: Linear Regression in Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in machine learning. By gaining a deep understanding of linear regression, learners can enhance their ability to analyze data, build predictive models, and make informed decisions, all of which are essential skills for a successful Data Scientist.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians collect, analyze, and interpret data. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in statistics. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to analyze data, build predictive models, and make informed decisions, all of which are essential skills for a successful Statistician.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in quantitative analysis. By gaining a deep understanding of linear regression, learners can enhance their ability to analyze data, build predictive models, and make informed decisions, all of which are essential skills for a successful Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Machine Learning Researcher

Machine Learning Researchers conduct research and develop new machine learning algorithms and techniques. This course can help learners develop a strong foundation in linear regression, which is a fundamental machine learning technique. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to develop and evaluate new machine learning algorithms and techniques.

See salaries and explore the career path for Machine Learning Researcher

Research Scientist

Research Scientists conduct research and develop new technologies. This course can help learners develop a strong foundation in linear regression, which is a fundamental technique in machine learning and statistics. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to analyze data, build predictive models, and make informed decisions, all of which are essential skills for a successful Research Scientist.

See salaries and explore the career path for Research Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. This course can help learners build a strong foundation in linear regression, which is a fundamental machine learning technique. By understanding the theory behind linear regression and how to implement it in Python, learners can develop the skills necessary to succeed in a Machine Learning Engineer role.

See salaries and explore the career path for Machine Learning Engineer

Operations Research Analyst

Operations Research Analysts use data to help businesses make informed decisions. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in operations research. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to analyze data, build predictive models, and make informed decisions, all of which are essential skills for a successful Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

Risk Analysts use data to help businesses identify and manage risks. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in risk analysis. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to analyze data, build predictive models, and make informed decisions, all of which are essential skills for a successful Risk Analyst.

See salaries and explore the career path for Risk Analyst

Market Researcher

Market Researchers use data to help businesses understand their customers and make informed decisions. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in market research. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to analyze data, build predictive models, and make informed decisions, all of which are essential skills for a successful Market Researcher.

See salaries and explore the career path for Market Researcher

Financial Analyst

Financial Analysts use data to help investors make informed decisions. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in financial analysis. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to analyze data, build predictive models, and make informed decisions, all of which are essential skills for a successful Financial Analyst.

See salaries and explore the career path for Financial Analyst

Data Analyst

Data Analysts use math, statistics, programming, and machine learning skills to help companies make data-driven decisions. This course can help learners develop the foundation in linear regression, which is a fundamental technique in machine learning. By understanding how to build and interpret linear regression models, learners can gain valuable insights from data and contribute to the success of a Data Analyst role.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in machine learning and data science. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to build and deploy software applications that leverage machine learning and data science techniques.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers use data to help businesses develop and launch successful products. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in product management. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to analyze data, build predictive models, and make informed decisions, all of which are essential skills for a successful Product Manager.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts use data to help businesses make informed decisions. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in business analytics. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to analyze data, build predictive models, and make informed decisions, all of which are essential skills for a successful Business Analyst.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines. This course can help learners develop a strong foundation in linear regression, which is a widely-used technique in data engineering. By understanding the theory behind linear regression and how to implement it in Python, learners can enhance their ability to build and deploy data pipelines that leverage machine learning and data science techniques.

See salaries and explore the career path for Data Engineer