Deep Learning Prerequisites: Logistic Regression in Python

Data science, machine learning, and artificial intelligence in Python for students and professionals

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Course Data

Lectures: 57
Length: 6h 42m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

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.





Suggested Prerequisites:

  • calculus
  • matrix arithmetic
  • probability
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations, loading a CSV file


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. ...

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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. ...

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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

Introduction and Outline

5 Lectures · 31min
  1. Introduction and Outline (07:23) (FREE preview available)
  2. How to Succeed in this Course (03:04)
  3. Statistics vs. Machine Learning (09:58)
  4. Review of the classification problem (01:54)
  5. Introduction to the E-Commerce Course Project (08:53)

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

10 Lectures · 01hr 02min
  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 (21:05)
  7. E-Commerce Course Project: Making Predictions (13:01)
  8. Feedforward Quiz (01:25)
  9. Prediction Section Summary (01:12)
  10. Suggestion Box (03:10)

Solving for the optimal weights

11 Lectures · 53min
  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 (02:47)
  5. The cross-entropy error function - Code (04:54)
  6. Visualizing the linear discriminant / Bayes classifier / Gaussian clouds (02:29)
  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 (13:30)
  11. Training Section Summary (02:03)

Practical concerns

11 Lectures · 54min
  1. Practical Section Introduction (02:46)
  2. Interpreting the Weights (04:08)
  3. L2 Regularization - Theory (08:39)
  4. L2 Regularization - Code (01:44)
  5. L1 Regularization - Theory (02:54)
  6. L1 Regularization - Code (06:14)
  7. L1 vs L2 Regularization (03:06)
  8. The donut problem (10:02)
  9. The XOR problem (06:13)
  10. Why Divide by Square Root of D? (06:32)
  11. Practical Section Summary (02:03)

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

2 Lectures · 08min
  1. Sentiment Analysis (05:14)
  2. Exercises + how to get good at this (02:49)

Project: Facial Expression Recognition

4 Lectures · 34min
  1. Facial Expression Recognition Problem Description (12:22)
  2. The class imbalance problem (06:02)
  3. Utilities walkthrough (05:46)
  4. Facial Expression Recognition in Code (10:42)

Helpful Review

1 Lectures · 04min
  1. Gradient Descent Tutorial (04:30)

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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