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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 realworld 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:
In the first section, I will show you how to use 1D 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 1D linear regression to anydimensional linear regression  in other words, how to create a machine learning model that can learn from multiple inputs.
We will apply multidimensional 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, traintest 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.
NOTES:
All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples
In the directory: linear_regression_class
Make sure you always "git pull" so you have the latest version!
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Section 1: Introduction and Outline  

Lecture 1 
Introduction and Outline
Preview

03:35  
Lecture 2  05:13  
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. 

Lecture 3 
Introduction to Moore's Law Problem
Preview

02:30  
Quiz 1 
What can linear regression be used for?

1 question  
Section 2: 1D Linear Regression: Theory and Code  
Lecture 4 
Define the model in 1D, derive the solution

14:52  
Lecture 5 
Coding the 1D solution in Python

07:38  
Lecture 6 
Determine how good the model is  rsquared

05:50  
Lecture 7 
Rsquared in code

02:15  
Lecture 8 
Demonstrating Moore's Law in Code

08:00  
Quiz 2 
Rsquared

1 question  
Section 3: Multiple linear regression and polynomial regression  
Lecture 9 
Define the multidimensional problem and derive the solution

17:07  
Lecture 10 
How to solve multiple linear regression using only matrices

01:55  
Lecture 11 
Coding the multidimensional solution in Python

07:29  
Lecture 12 
Polynomial regression  extending linear regression (with Python code)

07:56  
Lecture 13 
Predicting Systolic Blood Pressure from Age and Weight

05:45  
Quiz 3 
Rsquared

1 question  
Section 4: Practical machine learning issues  
Lecture 14 
Generalization error, train and test sets

02:49  
Lecture 15 
Generalization and Overfitting Demonstration in Code

07:32  
Lecture 16 
Categorical inputs

05:21  
Quiz 4 
Onehot encoding

1 question  
Lecture 17 
L2 Regularization  Theory

04:21  
Lecture 18 
L2 Regularization  Code

04:13  
Lecture 19 
The Dummy Variable Trap

03:58  
Lecture 20 
Gradient Descent Tutorial

04:30  
Lecture 21 
Gradient Descent for Linear Regression

02:13  
Lecture 22 
Bypass the Dummy Variable Trap with Gradient Descent

04:17  
Lecture 23 
L1 Regularization  Theory

03:05  
Lecture 24 
L1 Regularization  Code

04:25  
Lecture 25 
L1 vs L2 Regularization

03:05  
Section 5: Conclusion and Next Steps  
Lecture 26 
Brief overview of advanced linear regression and machine learning topics

05:14  
Lecture 27 
Exercises, practice, and how to get good at this

03:54  
Section 6: Appendix  
Lecture 28 
BONUS: Where to get Udemy coupons and FREE deep learning material
Preview

02:20  
Lecture 29 
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

17:22 
I am a data scientist, big data engineer, and full stack software engineer.
For my masters thesis I worked on braincomputer interfaces using machine learning. These assist nonverbal and nonmobile persons communicate with their family and caregivers.
I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various highthroughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict clickthrough rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.
Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.