Deep Learning Prerequisites: Logistic Regression in Python
What you will learn

program logistic regression from scratch in Python

describe how logistic regression is useful in data science

derive the error and update rule for logistic regression

understand how logistic regression works as an analogy for the biological neuron

use logistic regression to solve realworld business problems like predicting user actions from ecommerce data and facial expression recognition

understand why regularization is used in machine learning
Curriculum
Section 1: Start Here
Section 2: Basics: What is linear classification? What's the relation to neural networks?
Section 3: Solving for the optimal weights
Section 4: Practical concerns
Section 5: Checkpoint and applications: How to make sure you know your stuff
Section 6: Project: Facial Expression Recognition
Section 7: Appendix
Course Description
Data science techniques for professionals and students  learn the theory behind logistic regression and code in Python
Requirements
 Derivatives, matrix arithmetic, probability
 You should know some basic Python coding with the Numpy Stack
 Install numpy and matplotlib
Description
This course is a leadin to deep learning and neural networks  it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic 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 logistic regression module in Python.
This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.
This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.
Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!
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 use your skills to make datadriven decisions and optimize your business using scientific principles, then this course is for you.
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.
HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus (taking derivatives)

matrix arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file
TIPS (for getting through the course):

Watch it 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!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)
Who this course is for:
 Adult learners who want to get into the field of data science and big data
 Students who are thinking of pursuing machine learning or data science
 Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python
 People who know some machine learning but want to be able to relate it to artificial intelligence
 People who are interested in bridging the gap between computational neuroscience and machine learning