Section 1 : Start Here
Lecture 1 Introduction and Outline copy 00:04:02 Duration
Lecture 2 Anyone Can Succeed in this Course 00:11:55 Duration
Lecture 3 Statistics vs 00:09:59 Duration
Lecture 4 Review of the classification problem 00:01:53 Duration
Lecture 5 Introduction to the E-Commerce Course Project 00:08:22 Duration

Section 2 : Basics What is linear classification What's the relation to neural networks
Lecture 1 Linear Classification 00:04:50 Duration
Lecture 2 Biological inspiration - the neuron 00:03:36 Duration
Lecture 3 How do we calculate the output of a neuron logistic classifier - Theory 00:04:18 Duration
Lecture 4 How do we calculate the output of a neuron logistic classifier - Code 00:04:31 Duration
Lecture 5 Interpretation of Logistic Regression Output 00:05:33 Duration
Lecture 6 E-Commerce Course Project Pre-Processing the Data 00:05:24 Duration
Lecture 7 E-Commerce Course Project Making Predictions 00:03:01 Duration
Lecture 8 Feedforward Quiz
Lecture 9 Prediction Section Summary 00:01:11 Duration
Lecture 10 Suggestion Box 00:03:04 Duration

Section 3 : Solving for the optimal weights
Lecture 1 Training Section Introduction 00:01:38 Duration
Lecture 2 A closed-form solution to the Bayes classifier 00:05:59 Duration
Lecture 3 What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc
Lecture 4 The cross-entropy error function - Theory 00:02:46 Duration
Lecture 5 The cross-entropy error function - Code 00:04:53 Duration
Lecture 6 Visualizing the linear discriminant Bayes classifier Gaussian clouds 00:02:28 Duration
Lecture 7 Maximizing the likelihood 00:06:35 Duration
Lecture 8 Updating the weights using gradient descent - Theory 00:06:20 Duration
Lecture 9 Updating the weights using gradient descent - Code 00:03:10 Duration
Lecture 10 E-Commerce Course Project Training the Logistic Model 00:06:48 Duration
Lecture 11 Training Section Summary 00:02:02 Duration

Section 4 : Practical concerns
Lecture 1 Practical Section Introduction 00:02:45 Duration
Lecture 2 Interpreting the Weights 00:04:08 Duration
Lecture 3 L2 Regularization - Theory 00:08:39 Duration
Lecture 4 L2 Regularization - Code 00:01:43 Duration
Lecture 5 L1 Regularization - Theory 00:02:53 Duration
Lecture 6 L1 Regularization - Code 00:06:13 Duration
Lecture 7 L1 vs L2 Regularization 00:03:06 Duration
Lecture 8 The donut problem 00:10:02 Duration
Lecture 9 The XOR problem 00:06:12 Duration
Lecture 10 Why Divide by Square Root of D 00:06:32 Duration
Lecture 11 Practical Section Summary 00:02:02 Duration

Section 5 : Checkpoint and applications How to make sure you know your stuff
Lecture 1 BONUS Sentiment Analysis 00:05:14 Duration
Lecture 2 BONUS Exercises + how to get good at this 00:02:48 Duration

Section 6 : Project Facial Expression Recognition
Lecture 1 Facial Expression Recognition Project Introduction
Lecture 2 Facial Expression Recognition Problem Description 00:12:21 Duration
Lecture 3 The class imbalance problem 00:06:01 Duration
Lecture 4 Utilities walkthrough 00:05:45 Duration
Lecture 5 Facial Expression Recognition in Code 00:10:41 Duration
Lecture 6 Facial Expression Recognition Project Summary

Section 7 : Background Review
Lecture 1 Gradient Descent Tutorial 00:04:30 Duration

Section 8 : Setting Up Your Environment (FAQ by Student Request)
Lecture 1 Windows-Focused Environment Setup 2018 00:20:20 Duration
Lecture 2 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 00:17:33 Duration

Section 9 : Extra Help With Python Coding for Beginners (FAQ by Student Request)
Lecture 1 How to Uncompress a 00:03:18 Duration
Lecture 2 How to Code by Yourself (part 1) 00:15:54 Duration
Lecture 3 How to Code by Yourself (part 2) 00:09:23 Duration
Lecture 4 Proof that using Jupyter Notebook is the same as not using it 00:12:29 Duration
Lecture 5 Python 2 vs Python 3 00:04:38 Duration

Section 10 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Lecture 1 How to Succeed in this Course (Long Version) 00:10:24 Duration
Lecture 2 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 00:22:04 Duration
Lecture 3 Machine Learning and AI Prerequisite Roadmap (pt 1) 00:11:19 Duration
Lecture 4 Machine Learning and AI Prerequisite Roadmap (pt 2)

Section 11 : Appendix FAQ Finale
Lecture 1 What is the Appendix 00:02:48 Duration