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

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 6 Hours | 873 MB

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

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!

What you’ll 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

**Table of Contents**

**Start Here**

1 Introduction and Outline

2 How to Succeed in this Course

3 Review of the classification problem

4 Introduction to the E-Commerce Course Project

**Basics What is linear classification What’s the relation to neural networks**

5 Linear Classification

6 Biological inspiration – the neuron

7 How do we calculate the output of a neuron logistic classifier – Theory

8 How do we calculate the output of a neuron logistic classifier – Code

9 Interpretation of Logistic Regression Output

10 E-Commerce Course Project Pre-Processing the Data

11 E-Commerce Course Project Making Predictions

12 Feedforward Quiz

13 Prediction Section Summary

**Solving for the optimal weights**

14 Training Section Introduction

15 E-Commerce Course Project Training the Logistic Model

16 Training Section Summary

17 A closed-form solution to the Bayes classifier

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

19 The cross-entropy error function – Theory

20 The cross-entropy error function – Code

21 Visualizing the linear discriminant Bayes classifier Gaussian clouds

22 Maximizing the likelihood

23 Updating the weights using gradient descent – Theory

24 Updating the weights using gradient descent – Code

**Practical concerns**

25 Practical Section Introduction

26 Practical Section Summary

27 Interpreting the Weights

28 L2 Regularization – Theory

29 L2 Regularization – Code

30 L1 Regularization – Theory

31 L1 Regularization – Code

32 L1 vs L2 Regularization

33 The donut problem

34 The XOR problem

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

35 BONUS Sentiment Analysis

36 BONUS Where to get Udemy coupons and FREE deep learning material

37 BONUS Exercises + how to get good at this

**Project Facial Expression Recognition**

38 Facial Expression Recognition Project Introduction

39 Facial Expression Recognition Problem Description

40 The class imbalance problem

41 Utilities walkthrough

42 Facial Expression Recognition in Code

43 Facial Expression Recognition Project Summary

**Appendix**

44 What is the Appendix

45 Proof that using Jupyter Notebook is the same as not using it

46 Python 2 vs Python 3

47 What order should I take your courses in (part 1)

48 What order should I take your courses in (part 2)

49 Gradient Descent Tutorial

50 Windows-Focused Environment Setup 2018

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

52 How to Code by Yourself (part 1)

53 How to Code by Yourself (part 2)

54 How to Uncompress a .tar.gz file

55 How to Succeed in this Course (Long Version)

56 Is this for Beginners or Experts Academic or Practical Fast or slow-paced

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