# Deep Learning Prerequisites: Linear Regression in Python

### Register for this Course

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### Course Data

Lectures: 55
Length: 6h 26m
Skill Level: All Levels
Languages: English

### Course Description

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.

Suggested Prerequisites:

• calculus
• probability
• Python coding: if/else, loops, lists, dicts, sets

Tips for success:

• Use the video speed changer! Personally, I like to watch at 2x.
• Take handwritten notes. This will drastically increase your ability to retain the information.
• Write down the equations. If you don't, I guarantee it will just look like gibberish.
• Ask lots of questions on the discussion board. The more the better!
• Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
• Write code yourself, this is an applied course! Don't be a "couch potato".

## Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...

I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

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.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

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

3 Lectures · 20min
1. Introduction and Outline (07:42) (FREE preview available)
2. How to Succeed in this Course (03:04)
3. Statistics vs. Machine Learning (09:58)

#### 1-D Linear Regression: Theory and Code

12 Lectures · 01hr 11min
1. What is machine learning? How does linear regression play a role? (05:13)
2. Define the model in 1-D, derive the solution (Updated Version) (12:44)
3. Define the model in 1-D, derive the solution (14:53)
4. Coding the 1-D solution in Python (07:38)
5. Exercise: Theory vs. Code (01:20)
6. Determine how good the model is: r-squared (05:51)
7. R-squared in code (02:16)
8. Introduction to Moore's Law Problem (02:31)
9. Demonstrating Moore's Law in Code (08:01)
10. Moore's Law Derivation (06:02)
11. R-squared Quiz 1 (01:49)
12. Suggestion Box (03:10)

#### Multiple linear regression and polynomial regression

7 Lectures · 51min
1. Define the multi-dimensional problem and derive the solution (Updated Version) (09:35)
2. Define the multi-dimensional problem and derive the solution (17:08)
3. How to solve multiple linear regression using only matrices (01:56)
4. Coding the multi-dimensional solution in Python (07:30)
5. Polynomial regression - extending linear regression (with Python code) (07:57)
6. Predicting Systolic Blood Pressure from Age and Weight (05:46)
7. R-squared Quiz 2 (02:06)

#### Practical machine learning issues

17 Lectures · 01hr 14min
1. What do all these letters mean? (06:23)
2. Interpreting the Weights (04:01)
3. Generalization error, train and test sets (02:50)
4. Generalization and Overfitting Demonstration in Code (07:33)
5. Categorical inputs (05:22)
6. One-Hot Encoding Quiz (02:08)
7. Probabilistic Interpretation of Squared Error (05:16)
8. L2 Regularization - Theory (04:22)
9. L2 Regularization - Code (04:14)
10. The Dummy Variable Trap (03:59)
12. Gradient Descent for Linear Regression (02:14)
13. Bypass the Dummy Variable Trap with Gradient Descent (04:18)
14. L1 Regularization - Theory (03:06)
15. L1 Regularization - Code (04:26)
16. L1 vs L2 Regularization (03:06)
17. Why Divide by Square Root of D? (06:32)

#### Conclusion and Next Steps

3 Lectures · 14min
1. Brief overview of advanced linear regression and machine learning topics (05:15)
2. Exercises, practice, and how to get good at this (03:55)
3. Data Science Interview Question - Residuals (04:50)

#### Setting Up Your Environment (Appendix/FAQ by Student Request)

3 Lectures · 42min
1. Pre-Installation Check (04:13)
2. Anaconda Environment Setup (20:21)
3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

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

4 Lectures · 42min
1. How to Code Yourself (part 1) (15:55)
2. How to Code Yourself (part 2) (09:24)
3. Proof that using Jupyter Notebook is the same as not using it (12:29)
4. Python 2 vs Python 3 (04:38)

#### Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
1. How to Succeed in this Course (Long Version) (10:25)
2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
3. What order should I take your courses in? (part 1) (11:19)
4. What order should I take your courses in? (part 2) (16:07)

#### Appendix / FAQ Finale

2 Lectures · 08min
1. What is the Appendix? (02:48)
2. Where to get discount coupons and FREE deep learning material (05:49)

#### Extras

• Calculus Cheatsheet
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