Deep Learning Prerequisites: Logistic Regression in Python
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Deep Learning Prerequisites: Logistic Regression in Python

Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python
Best Seller
4.6 (1,011 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
10,706 students enrolled
Last updated 8/2017
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Current price: $10 Original price: $120 Discount: 92% off
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  • 4 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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 real-world business problems like predicting user actions from e-commerce data and facial expression recognition
  • understand why regularization is used in machine learning
View Curriculum
  • You should know how to take a derivative
  • You should know some basic Python coding
  • Install numpy and matplotlib

This course is a lead-in 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 real-world 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 data-driven 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.


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!


  • calculus
  • linear algebra
  • 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.


  • (The Numpy Stack in Python)
  • Linear Regression in Python
  • Logistic Regression in Python
  • (Supervised Machine Learning in Python)
  • (Bayesian Machine Learning in Python: A/B Testing)
  • Deep Learning in Python
  • Practical Deep Learning in Theano and TensorFlow
  • (Supervised Machine Learning in Python 2: Ensemble Methods)
  • Convolutional Neural Networks in Python
  • (Easy NLP)
  • (Cluster Analysis and Unsupervised Machine Learning)
  • Unsupervised Deep Learning
  • (Hidden Markov Models)
  • Recurrent Neural Networks in Python
  • Artificial Intelligence: Reinforcement Learning in Python
  • Natural Language Processing with Deep Learning in Python

Who is the target audience?
  • 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 interested in pursuing statistics and coding in Python instead of R
  • 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
Curriculum For This Course
43 Lectures
Start Here
4 Lectures 20:15

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.

Preview 04:02

How to Succeed in this Course

Difference between supervised and unsupervised learning. Difference between classification and regression. Feel free to skip this one if you already know this.

Preview 01:53

Introduction to the E-Commerce Course Project

An easy first quiz

Easy first quiz
1 question
Basics: What is linear classification? What's the relation to neural networks?
8 Lectures 28:12

I discuss what linear classification is from a general standpoint, without invoking any specifics related to logistic regression. I provide a 2-dimensional 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.

Linear Classification

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.

Biological inspiration - the neuron

I show the feedforward calculation for the output of a logistic unit.

How do we calculate the output of a neuron / logistic classifier? - Theory

I show how to code the feedforward calculation for the output of a logistic unit in Python and numpy.

How do we calculate the output of a neuron / logistic classifier? - Code

E-Commerce Course Project: Pre-Processing the Data

E-Commerce Course Project: Making Predictions

Feedforward Quiz

Prediction Section Summary
Solving for the optimal weights
10 Lectures 44:11
Training Section Introduction

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, Gaussian-distributed).

A closed-form solution to the Bayes classifier

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.

What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc.

I show the cross-entropy error formula, and describe why this is used as an appropriate objective function for logistic regression.

The cross-entropy error function - Theory

I show how to calculate the cross-entropy error of your model in Python and numpy.

The cross-entropy error function - Code

Visualizing the linear discriminant / Bayes classifier / Gaussian clouds

I show how to derive the likelihood and log-likelihood of your model and data, and I show how maximizing the likelihood is equivalent to minimizing the cross-entropy.

Maximizing the likelihood

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.

Updating the weights using gradient descent - Theory

I show how to code weight updates for logistic regression using gradient descent in Python and numpy.

Updating the weights using gradient descent - Code

E-Commerce Course Project: Training the Logistic Model
Practical concerns
10 Lectures 47:39
Practical Section Introduction

Interpreting the Weights

L2 Regularization - Theory

I show how to apply regularization for logistic regression in Python.

L2 Regularization - Code

L1 Regularization - Theory

L1 Regularization - Code

L1 vs L2 Regularization

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

The donut problem

This lecture describes how to apply logistic regression to the XOR problem by making it a 3-dimensional problem.

The XOR problem

Practical Section Summary
Checkpoint and applications: How to make sure you know your stuff
3 Lectures 10:21

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.

Preview 05:13

BONUS: Exercises + how to get good at this
Project: Facial Expression Recognition
4 Lectures 34:48
Facial Expression Recognition Problem Description

The class imbalance problem

Utilities walkthrough

Facial Expression Recognition in Code
4 Lectures 47:19
Gradient Descent Tutorial

How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

How to Code by Yourself (part 1)

How to Code by Yourself (part 2)
About the Instructor
Lazy Programmer Inc.
4.6 Average rating
14,218 Reviews
75,596 Students
19 Courses
Data scientist and big data engineer

I am a data scientist, big data engineer, and full stack software engineer.

I have a masters degree in computer engineering with a specialization in machine learning and pattern recognition.

I have 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. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've 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.

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.