Deep Learning Prerequisites: Logistic Regression in Python

Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python

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Course Data

Lectures: 53
Length: 05h 38m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, 30-day money back guarantee

Course Description

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.





HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

  • 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.

Lectures

Introduction and Outline

  1. Introduction and Outline (04:03) (FREE preview available)
  2. How to Succeed in this Course (03:13)
  3. Review of the classification problem (01:53)
  4. Introduction to the E-Commerce Course Project (08:53)

Basics: What is linear classification? What's the relation to neural networks?

  1. Linear Classification (04:50)
  2. Biological inspiration - the neuron (03:37)
  3. How do we calculate the output of a neuron / logistic classifier? - Theory (04:19)
  4. How do we calculate the output of a neuron / logistic classifier? - Code (04:31)
  5. Interpretation of Logistic Regression Output (05:33)
  6. E-Commerce Course Project: Pre-Processing the Data (05:24)
  7. E-Commerce Course Project: Making Predictions (03:01)
  8. Feedforward Quiz (01:25)
  9. Prediction Section Summary (01:11)

Solving for the optimal weights

  1. Training Section Introduction (01:39)
  2. A closed-form solution to the Bayes classifier (06:00)
  3. What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc. (03:38)
  4. The cross-entropy error function - Theory (02:46)
  5. The cross-entropy error function - Code (04:53)
  6. Visualizing the linear discriminant / Bayes classifier / Gaussian clouds (02:28)
  7. Maximizing the likelihood (06:35)
  8. Updating the weights using gradient descent - Theory (06:21)
  9. Updating the weights using gradient descent - Code (03:10)
  10. E-Commerce Course Project: Training the Logistic Model (06:48)
  11. Training Section Summary (02:02)

Practical concerns

  1. Practical Section Introduction (02:46)
  2. Interpreting the Weights (04:08)
  3. L2 Regularization - Theory (08:39)
  4. L2 Regularization - Code (01:43)
  5. L1 Regularization - Theory (02:53)
  6. L1 Regularization - Code (06:14)
  7. L1 vs L2 Regularization (03:06)
  8. The donut problem (10:02)
  9. The XOR problem (06:12)
  10. Practical Section Summary (02:02)

Checkpoint and applications: How to make sure you know your stuff

  1. Sentiment Analysis (05:14)
  2. Exercises + how to get good at this (02:48)

Project: Facial Expression Recognition

  1. Facial Expression Recognition Problem Description (12:22)
  2. The class imbalance problem (06:01)
  3. Utilities walkthrough (05:45)
  4. Facial Expression Recognition in Code (10:42)

Helpful Review

  1. Gradient Descent Tutorial (04:30)

Appendix

  1. What is the Appendix? (02:48)
  2. Windows-Focused Environment Setup 2018 (20:21)
  3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)
  4. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:04)
  5. How to Code Yourself (part 1) (15:55)
  6. How to Code Yourself (part 2) (09:23)
  7. Proof that using Jupyter Notebook is the same as not using it (12:29)
  8. What order should I take your courses in? (part 1) (11:19)
  9. What order should I take your courses in? (part 2) (16:07)
  10. Python 2 vs Python 3 (04:38)
  11. How to Succeed in this Course (Long Version) (10:25)
  12. Where to get discount coupons and FREE deep learning material (02:21)