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Deep Learning is a set of powerful algorithms that are the force behind self-driving cars, image searching, voice recognition, and many, many more applications we consider decidedly "futuristic." One of the central foundations of deep learning is linear regression; using probability theory to gain deeper insight into the "line of best fit." This is the first step to building machines that, in effect, act like neurons in a neural network as they *learn* while they're fed more information. In this course, you'll start with the basics of building a linear regression module in Python, and progress into practical machine learning issues that will provide the foundations for an exploration of Deep Learning.

*Like what you're learning? Try out the **The Advanced Guide to Deep Learning and Artificial Intelligence* next.

- Access 20 lectures & 2 hours of content 24/7
- Use a 1-D linear regression to prove Moore's Law
- Learn how to create a machine learning model that can learn from multiple inputs
- Apply multi-dimensional linear regression to predict a patient's systolic blood pressure given their age & weight
- Discuss generalization, overfitting, train-test splits, & other issues that may arise while performing data analysis

The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has 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. He has created new big data pipelines using Hadoop/Pig/MapReduce, and 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.

He has 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 his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

He has 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. He has created new big data pipelines using Hadoop/Pig/MapReduce, and 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.

He has 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 his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Details & Requirements

- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels, but you must have some knowledge of calculus, linear algebra, probability, Python, and Numpy
- All code for this course is available for download
*here*, in the directory linear_regression_class

Compatibility

- Internet required

Terms

- Instant digital redemption

- Introduction and Outline
- Introduction and Outline (3:36)
- What is machine learning? How does linear regression play a role? (5:13)
- Introduction to Moore's Law Problem (2:30)

- 1-D Linear Regression: Theory and Code
- Define the model in 1-D, derive the solution (14:52)
- Coding the 1-D solution in Python (7:38)
- Determine how good the model is - r-squared (5:51)
- R-squared in code (2:15)
- Demonstrating Moore's Law in Code (8:00)
- R-Squared Quiz

- Multiple linear regression and polynomial regression
- Define the multi-dimensional problem and derive the solution (17:07)
- How to solve multiple linear regression using only matrices (1:55)
- Coding the multi-dimensional solution in Python (7:29)
- Polynomial regression - extending linear regression (with Python code) (7:56)
- Predicting Systolic Blood Pressure from Age and Weight (5:45)
- R-Squared Quiz 2

- Practical machine learning issues
- Generalization error, train and test sets (2:49)
- Generalization and Overfitting Demonstration in Code (7:32)
- Categorical inputs (5:21)
- Brief overview of advanced linear regression and machine learning topics (5:15)
- Exercises, practice, and how to get good at this (3:54)
- One-hot encoding

- Appendix
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)

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