Section 1 : Welcome

 		Lecture 1 Introduction and Outline copy 2:42
Lecture 2 Where to get the code 8:27
Lecture 3 Anyone Can Succeed in this Course 11:55

Section 2 : Google Colab

 		Lecture 4 Intro to Google Colab, how to use a GPU or TPU for free
Lecture 5 Uploading your own data to Google Colab
Lecture 6 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn 8:54

Section 3 : Machine Learning and Neurons

 		Lecture 7 Review Section Introduction 2:38
Lecture 8 What is Machine Learning 14:26
Lecture 9 Code Preparation (Classification Theory) 15:59
Lecture 10 Beginner's Code Preamble 4:38
Lecture 11 Classification Notebook 8:40
Lecture 12 Code Preparation (Regression Theory) 7:19
Lecture 13 Regression Notebook 10:35
Lecture 14 The Neuron
Lecture 15 How does a model learn 10:54
Lecture 16 Making Predictions 6:45
Lecture 17 Saving and Loading a Model 4:28
Lecture 18 Suggestion Box 3:4

Section 4 : Feedforward Artificial Neural Networks

 		Lecture 19 Artificial Neural Networks Section Introduction 6:0
Lecture 20 Forward Propagation 9:40
Lecture 21 The Geometrical Picture 9:44
Lecture 22 Activation Functions 17:18
Lecture 23 Multiclass Classification 8:41
Lecture 24 How to Represent Images 12:37
Lecture 25 Code Preparation (ANN) 12:42
Lecture 26 ANN for Image Classification 8:37
Lecture 27 ANN for Regression 11:5

Section 5 : Convolutional Neural Networks

 		Lecture 28 What is Convolution (part 1) 16:38
Lecture 29 What is Convolution (part 2) 5:57
Lecture 30 What is Convolution (part 3) 6:41
Lecture 31 Convolution on Color Images 15:59
Lecture 32 CNN Architecture 20:58
Lecture 33 CNN Code Preparation 15:13
Lecture 34 CNN for Fashion MNIST 6:46
Lecture 35 CNN for CIFAR-10 4:28
Lecture 36 Data Augmentation 8:51
Lecture 37 Batch Normalization 5:14
Lecture 38 Improving CIFAR-10 Results 10:22

Section 6 : Natural Language Processing (NLP)

 		Lecture 39 Embeddings 13:12
Lecture 40 Code Preparation (NLP) 13:18
Lecture 41 Text Preprocessing 5:30
Lecture 42 CNNs for Text 8:8
Lecture 43 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 7 : Convolution In-Depth

 		Lecture 44 Real-Life Examples of Convolution 8:53
Lecture 45 Beginner's Guide to Convolution 6:27
Lecture 46 Alternative Views on Convolution 6:42

Section 8 : Convolutional Neural Network Description

 		Lecture 47 Convolution on 3-D Images 10:50
Lecture 48 Tracking Shapes in a CNN 16:37

Section 9 : Practical Tips

 		Lecture 49 Advanced CNNs and how to Design your Own 11:10

Section 10 : In-Depth Loss Functions

 		Lecture 50 Mean Squared Error 9:12
Lecture 51 Binary Cross Entropy 5:58
Lecture 52 Categorical Cross Entropy 8:7

Section 11 : In-Depth Gradient Descent

 		Lecture 53 Gradient Descent 7:52
Lecture 54 Stochastic Gradient Descent 4:37
Lecture 55 Momentum 6:10
Lecture 56 Variable and Adaptive Learning Rates 11:45
Lecture 57 Adam

Section 12 : Extras

 		Lecture 58 Colab Notebooks Text

Section 13 : Setting Up Your Environment (FAQ by Student Request)

 		Lecture 59 Windows-Focused Environment Setup 2018 20:20
Lecture 60 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:33

Section 14 : Extra Help With Python Coding for Beginners (FAQ by Student Request)

 		Lecture 61 How to Code by Yourself (part 1) 15:54
Lecture 62 How to Code by Yourself (part 2) 9:23
Lecture 63 How to Uncompress a 3:18
Lecture 64 Proof that using Jupyter Notebook is the same as not using it 12:29
Lecture 65 Python 2 vs Python 3 4:38
Lecture 66 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 15 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)

 		Lecture 67 How to Succeed in this Course (Long Version) 10:24
Lecture 68 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 22:4
Lecture 69 Machine Learning and AI Prerequisite Roadmap (pt 1)
Lecture 70 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:7

Section 16 : Appendix FAQ Finale

 		Lecture 71 What is the Appendix 2:48
Lecture 72 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf