Deep Learning Prerequisites- Linear Regression in Python

Deep Learning Prerequisites: Linear Regression in Python, available at $119.99, has an average rating of 4.7, with 57 lectures, 4 quizzes, based on 6489 reviews, and has 35725 subscribers.

You will learn about 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 This course is ideal for individuals who are People who are interested in data science, machine learning, statistics and artificial intelligence or People new to data science who would like an easy introduction to the topic or People who wish to advance their career by getting into one of technologys trending fields, data science or Self-taught programmers who want to improve their computer science theoretical skills or Analytics experts who want to learn the theoretical basis behind one of statistics most-used algorithms It is particularly useful for People who are interested in data science, machine learning, statistics and artificial intelligence or People new to data science who would like an easy introduction to the topic or People who wish to advance their career by getting into one of technologys trending fields, data science or Self-taught programmers who want to improve their computer science theoretical skills or Analytics experts who want to learn the theoretical basis behind one of statistics most-used algorithms.

Enroll now: Deep Learning Prerequisites: Linear Regression in Python

Summary

Title: Deep Learning Prerequisites: Linear Regression in Python

Price: $119.99

Average Rating: 4.7

Number of Lectures: 57

Number of Quizzes: 4

Number of Published Lectures: 54

Number of Published Quizzes: 1

Number of Curriculum Items: 61

Number of Published Curriculum Objects: 55

Original Price: $199.99

Quality Status: approved

Status: Live

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

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

Who Should Attend

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 technologys 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

Target Audiences

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 technologys 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

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)

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction and Outline

Lecture 2: How to Succeed in this Course

Lecture 3: Statistics vs. Machine Learning

Chapter 2: 1-D Linear Regression: Theory and Code

Lecture 1: What is machine learning? How does linear regression play a role?

Lecture 2: Define the model in 1-D, derive the solution (Updated Version)

Lecture 3: Define the model in 1-D, derive the solution

Lecture 4: Coding the 1-D solution in Python

Lecture 5: Exercise: Theory vs. Code

Lecture 6: Determine how good the model is – r-squared

Lecture 7: R-squared in code

Lecture 8: Introduction to Moores Law Problem

Lecture 9: Demonstrating Moores Law in Code

Lecture 10: Moores Law Derivation

Lecture 11: R-squared Quiz 1

Lecture 12: Suggestion Box

Chapter 3: Multiple linear regression and polynomial regression

Lecture 1: Define the multi-dimensional problem and derive the solution (Updated Version)

Lecture 2: Define the multi-dimensional problem and derive the solution

Lecture 3: How to solve multiple linear regression using only matrices

Lecture 4: Coding the multi-dimensional solution in Python

Lecture 5: Polynomial regression – extending linear regression (with Python code)

Lecture 6: Predicting Systolic Blood Pressure from Age and Weight

Lecture 7: R-squared Quiz 2

Chapter 4: Practical machine learning issues

Lecture 1: What do all these letters mean?

Lecture 2: Interpreting the Weights

Lecture 3: Generalization error, train and test sets

Lecture 4: Generalization and Overfitting Demonstration in Code

Lecture 5: Categorical inputs

Lecture 6: One-Hot Encoding Quiz

Lecture 7: Probabilistic Interpretation of Squared Error

Lecture 8: L2 Regularization – Theory

Lecture 9: L2 Regularization – Code

Lecture 10: The Dummy Variable Trap

Lecture 11: Gradient Descent Tutorial

Lecture 12: Gradient Descent for Linear Regression

Lecture 13: Bypass the Dummy Variable Trap with Gradient Descent

Lecture 14: L1 Regularization – Theory

Lecture 15: L1 Regularization – Code

Lecture 16: L1 vs L2 Regularization

Lecture 17: Why Divide by Square Root of D?

Chapter 5: Conclusion and Next Steps

Lecture 1: Brief overview of advanced linear regression and machine learning topics

Lecture 2: Exercises, practice, and how to get good at this

Chapter 6: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Chapter 7: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Lecture 3: Proof that using Jupyter Notebook is the same as not using it

Lecture 4: Python 2 vs Python 3

Chapter 8: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)

Chapter 9: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 49 votes

2 stars: 61 votes

3 stars: 284 votes

4 stars: 2273 votes

5 stars: 3822 votes

Frequently Asked Questions

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