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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.
NOTES:
All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples
In the directory: logistic_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):
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Section 1: Start Here  

Lecture 1  04:02  
This lecture will outline what will be learned in the course. I explain the importance of knowing the math, and provide short descriptions of each section later covered. Feel free to skip it and jump right into the 3rd video. 

Lecture 2  01:53  
Difference between supervised and unsupervised learning. Difference between classification and regression. Feel free to skip this one if you already know this. 

Lecture 3 
Introduction to the ECommerce Course Project

08:52  
Quiz 1  1 question  
An easy first quiz 

Section 2: Basics: What is linear classification? What's the relation to neural networks?  
Lecture 4  04:49  
I discuss what linear classification is from a general standpoint, without invoking any specifics related to logistic regression. I provide a 2dimensional binary classification example and go over how we would classify data into 1 of 3 cases: positive class, negative class, and don't know / not sure. 

Lecture 5  03:36  
In this lecture I discuss a brief history of neural networks, and talk about how the characteristics of the neuron (action potential, signal propagation, inhibitory and excitatory behavior) are modeled in different ways: the Hodgkin Huxley mdoel, the FitzHugh Nagumo model, and the logistic model. 

Lecture 6  04:18  
I show the feedforward calculation for the output of a logistic unit. 

Lecture 7  04:30  
I show how to code the feedforward calculation for the output of a logistic unit in Python and numpy. 

Lecture 8 
ECommerce Course Project: PreProcessing the Data

05:24  
Lecture 9 
ECommerce Course Project: Making Predictions

03:00  
Quiz 2  1 question  
On the basic logistic formula 

Section 3: Solving for the optimal weights  
Lecture 10  05:59  
I show how we can solve for the weights in a logistic regression model if we make assumptions about the distributions of the input data (equal variance, Gaussiandistributed). 

Lecture 11  03:37  
All these symbols can get confusing so this is a lecture to give a short and simple description of what each letter "stands for". These are important to get right now, because we'll use it for this course and every later course on deep learning and machine learning. 

Lecture 12  02:46  
I show the crossentropy error formula, and describe why this is used as an appropriate objective function for logistic regression. 

Lecture 13  04:53  
I show how to calculate the crossentropy error of your model in Python and numpy. 

Lecture 14 
Visualizing the linear discriminant / Bayes classifier / Gaussian clouds

02:28  
Quiz 3  1 question  
Different types of error functions 

Lecture 15  06:34  
I show how to derive the likelihood and loglikelihood of your model and data, and I show how maximizing the likelihood is equivalent to minimizing the crossentropy. 

Lecture 16  06:20  
I explain how gradient descent can be used to solve for the minima of a function, and the algorithm that can be used to iteratively update the logistic weights. 

Lecture 17  03:09  
I show how to code weight updates for logistic regression using gradient descent in Python and numpy. 

Lecture 18 
ECommerce Course Project: Training the Logistic Model

06:47  
Quiz 4  1 question  
What happens when you have more than 2 classes in your output? 

Section 4: Practical concerns  
Lecture 19 
L2 Regularization  Theory

08:38  
Lecture 20  01:43  
I show how to apply regularization for logistic regression in Python. 

Lecture 21  10:01  
I show how logistic regression can be used to solve the donut problem (where one class is circular and is inside another circular class). 

Lecture 22  06:12  
This lecture describes how to apply logistic regression to the XOR problem by making it a 3dimensional problem. 

Quiz 5  1 question  
Why are neural networks better than logistic regression? 

Section 5: Checkpoint and applications: How to make sure you know your stuff  
Lecture 23  05:13  
This is a clip from my natural language processing course. It shows you how to apply logistic regression to sentiment analysis  measuring how positive or negative a word is. 

Lecture 24 
BONUS: Where to get Udemy coupons and FREE deep learning material
Preview

01:40  
Lecture 25 
BONUS: Exercises + how to get good at this

02:48  
Section 6: Project: Facial Expression Recognition  
Lecture 26 
Facial Expression Recognition Problem Description

12:21  
Lecture 27 
The class imbalance problem

06:01  
Lecture 28 
Utilities walkthrough

05:45  
Lecture 29 
Facial Expression Recognition in Code

10:41  
Section 7: Appendix  
Lecture 30 
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

17:22  
Lecture 31 
Gradient Descent Tutorial

04:30 
I am a data scientist, big data engineer, and full stack software engineer.
For my masters thesis I worked on braincomputer interfaces using machine learning. These assist nonverbal and nonmobile 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 highthroughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict clickthrough 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.
Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.