# Linear Programming for Linear Regression in Python

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### Course Data

Lectures: 12
Length: 1h 07m
Skill Level: All Levels
Languages: English

### Course Description

One of the most common questions I get in my Linear Regression class is, "What if we use the absolute error instead of the squared error?"

The answer is: this is entirely possible, but it requires an entirely different solution method.

These techniques are not usually taught in machine learning, yet they are essential to many fields such as operations research, quantitative finance, engineering, manufacturing, logistics, and more.

The main technique we will learn how to apply is called Linear Programming.

We will study several alternative loss functions for linear models, such as the L1 (absolute) loss, maximum absolute deviation (MAD), and the exponential loss for when you want your error to be positive-only or negative-only.

These techniques are part of a broader field of study known as convex optimization.

I hope you will join me in learning this essential skill for today's data science and quantitative professionals.

See you in class!

Suggested Prerequisites:

• calculus
• probability
• be able to derive linear regression on paper and code linear regression in Python
• Python coding: if/else, loops, lists, dicts, sets

## Testimonials and Success Stories

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Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

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Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...

By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.

### Lectures

#### Welcome and Review

2 Lectures · 12min

#### Linear Programming for Linear Regression

10 Lectures · 54min
1. Linear Programming Example (08:21)
2. Linear Programming Example in Code (04:57)
3. Absolute Error (L1 Loss) Maximum Likelihood (03:02)
4. Absolute Error (L1 Loss) Linear Program (08:03)
5. Absolute Error (L1 Loss) Code (06:01)
6. Maximum Absolute Deviation Theory (05:05)
7. Maximum Absolute Deviation Code (02:45)
8. Exponential Maximum Likelihood (04:39)
9. Exponential Linear Program (07:32)
10. Exponential Code (04:03)

#### Extras

• Linear Programming Example Notebook
• Linear Programming Linear Regression Notebook
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