4.63 out of 5
4.63
4808 reviews on Udemy

Deep Learning Prerequisites: Linear Regression in Python

Data science, machine learning, and artificial intelligence in Python for students and professionals
Instructor:
Lazy Programmer Inc.
27,201 students enrolled
English [Auto] More
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.

“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)

Welcome

1
Introduction and Outline
2
How to Succeed in this Course
3
Statistics vs. Machine Learning

1-D Linear Regression: Theory and Code

1
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.

2
What can linear regression be used for?
3
Define the model in 1-D, derive the solution (Updated Version)
4
Define the model in 1-D, derive the solution
5
Coding the 1-D solution in Python
6
Exercise: Theory vs. Code
7
Determine how good the model is - r-squared
8
R-squared in code
9
Introduction to Moore's Law Problem
10
Demonstrating Moore's Law in Code
11
Moore's Law Derivation
12
R-squared Quiz 1
13
Suggestion Box

Multiple linear regression and polynomial regression

1
Define the multi-dimensional problem and derive the solution (Updated Version)
2
Define the multi-dimensional problem and derive the solution
3
How to solve multiple linear regression using only matrices
4
Coding the multi-dimensional solution in Python
5
Polynomial regression - extending linear regression (with Python code)
6
Predicting Systolic Blood Pressure from Age and Weight
7
R-squared Quiz 2

Practical machine learning issues

1
What do all these letters mean?
2
Interpreting the Weights
3
Generalization error, train and test sets
4
Generalization and Overfitting Demonstration in Code
5
Categorical inputs
6
One-Hot Encoding Quiz
7
Probabilistic Interpretation of Squared Error
8
L2 Regularization - Theory
9
L2 Regularization - Code
10
The Dummy Variable Trap
11
Gradient Descent Tutorial
12
Gradient Descent for Linear Regression
13
Bypass the Dummy Variable Trap with Gradient Descent
14
L1 Regularization - Theory
15
L1 Regularization - Code
16
L1 vs L2 Regularization
17
Why Divide by Square Root of D?

Conclusion and Next Steps

1
Brief overview of advanced linear regression and machine learning topics
2
Exercises, practice, and how to get good at this

Setting Up Your Environment (FAQ by Student Request)

1
Windows-Focused Environment Setup 2018
2
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

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

1
How to Code by Yourself (part 1)
2
How to Code by Yourself (part 2)
3
Proof that using Jupyter Notebook is the same as not using it
4
Python 2 vs Python 3

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

1
How to Succeed in this Course (Long Version)
2
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
3
Machine Learning and AI Prerequisite Roadmap (pt 1)
4
Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix / FAQ Finale

1
What is the Appendix?
2
BONUS: Where to get Udemy coupons and FREE deep learning material
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