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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 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:
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.
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!
HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:
TIPS (for getting through the course):
USEFUL COURSE ORDERING:
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|Section 1: Introduction and Outline|
Introduction and OutlinePreview
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.
Introduction to Moore's Law ProblemPreview
What can linear regression be used for?
|Section 2: 1-D Linear Regression: Theory and Code|
Define the model in 1-D, derive the solution
Coding the 1-D solution in Python
Determine how good the model is - r-squared
R-squared in code
Demonstrating Moore's Law in Code
|Section 3: Multiple linear regression and polynomial regression|
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
|Section 4: Practical machine learning issues|
Generalization error, train and test sets
Generalization and Overfitting Demonstration in Code
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
|Section 5: Conclusion and Next Steps|
Brief overview of advanced linear regression and machine learning topics
Exercises, practice, and how to get good at this
|Section 6: Appendix|
BONUS: Where to get Udemy coupons and FREE deep learning materialPreview
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
I am a data scientist, big data engineer, and full stack software engineer.
For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile 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 high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through 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.