Section 1 : Start Here

 		Lecture 1 Introduction and Outline copy 4:2
Lecture 2 Anyone Can Succeed in this Course 11:55
Lecture 3 Statistics vs 9:59
Lecture 4 Review of the classification problem 1:53
Lecture 5 Introduction to the E-Commerce Course Project 8:22

Section 2 : Basics What is linear classification What's the relation to neural networks

 		Lecture 6 Linear Classification 4:50
Lecture 7 Biological inspiration - the neuron 3:36
Lecture 8 How do we calculate the output of a neuron logistic classifier - Theory 4:18
Lecture 9 How do we calculate the output of a neuron logistic classifier - Code 4:31
Lecture 10 Interpretation of Logistic Regression Output 5:33
Lecture 11 E-Commerce Course Project Pre-Processing the Data 5:24
Lecture 12 E-Commerce Course Project Making Predictions 3:1
Lecture 13 Feedforward Quiz
Lecture 14 Prediction Section Summary 1:11
Lecture 15 Suggestion Box 3:4

Section 3 : Solving for the optimal weights

 		Lecture 16 Training Section Introduction 1:38
Lecture 17 A closed-form solution to the Bayes classifier 5:59
Lecture 18 What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc
Lecture 19 The cross-entropy error function - Theory 2:46
Lecture 20 The cross-entropy error function - Code 4:53
Lecture 21 Visualizing the linear discriminant Bayes classifier Gaussian clouds 2:28
Lecture 22 Maximizing the likelihood 6:35
Lecture 23 Updating the weights using gradient descent - Theory 6:20
Lecture 24 Updating the weights using gradient descent - Code 3:10
Lecture 25 E-Commerce Course Project Training the Logistic Model 6:48
Lecture 26 Training Section Summary 2:2

Section 4 : Practical concerns

 		Lecture 27 Practical Section Introduction 2:45
Lecture 28 Interpreting the Weights 4:8
Lecture 29 L2 Regularization - Theory 8:39
Lecture 30 L2 Regularization - Code 1:43
Lecture 31 L1 Regularization - Theory 2:53
Lecture 32 L1 Regularization - Code 6:13
Lecture 33 L1 vs L2 Regularization 3:6
Lecture 34 The donut problem 10:2
Lecture 35 The XOR problem 6:12
Lecture 36 Why Divide by Square Root of D 6:32
Lecture 37 Practical Section Summary 2:2

Section 5 : Checkpoint and applications How to make sure you know your stuff

 		Lecture 38 BONUS Sentiment Analysis 5:14
Lecture 39 BONUS Exercises + how to get good at this 2:48

Section 6 : Project Facial Expression Recognition

 		Lecture 40 Facial Expression Recognition Project Introduction
Lecture 41 Facial Expression Recognition Problem Description 12:21
Lecture 42 The class imbalance problem 6:1
Lecture 43 Utilities walkthrough 5:45
Lecture 44 Facial Expression Recognition in Code 10:41
Lecture 45 Facial Expression Recognition Project Summary

Section 7 : Background Review

 		Lecture 46 Gradient Descent Tutorial 4:30

Section 8 : Setting Up Your Environment (FAQ by Student Request)

 		Lecture 47 Windows-Focused Environment Setup 2018 20:20
Lecture 48 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:33

Section 9 : Extra Help With Python Coding for Beginners (FAQ by Student Request)

 		Lecture 49 How to Uncompress a 3:18
Lecture 50 How to Code by Yourself (part 1) 15:54
Lecture 51 How to Code by Yourself (part 2) 9:23
Lecture 52 Proof that using Jupyter Notebook is the same as not using it 12:29
Lecture 53 Python 2 vs Python 3 4:38

Section 10 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)

 		Lecture 54 How to Succeed in this Course (Long Version) 10:24
Lecture 55 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 22:4
Lecture 56 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:19
Lecture 57 Machine Learning and AI Prerequisite Roadmap (pt 2)

Section 11 : Appendix FAQ Finale

 		Lecture 58 What is the Appendix 2:48