Section 1 : Course Overview, Installs, and Setup

lecture 1 INTRODUCTION TO BRAINMEASURES PROCT Pdf
lecture 2 About Certification Pdf
lecture 3 Course Setup and Installation
lecture 4 FAQ - Frequently Asked Questions Text

Section 2 : NumPy Crash Course

lecture 5 Introduction to NumPy 2:18
lecture 6 umPy Arrays 18:50
lecture 7 Numpy Index Selection 11:0
lecture 8 NumPy Operations 8:8
lecture 9 NumPy Exercises 1:8
lecture 10 Numpy Exercises - Solutions 7:0

Section 3 : Pandas Crash Course

lecture 11 Introduction to Pandas 3:52
lecture 12 Pandas Series 8:35
lecture 13 Pandas DataFrames - Part One 11:8
lecture 14 Pandas DataFrames - Part Two
lecture 15 Pandas Missing Data 10:2
lecture 16 GroupBy Operations 9:35
lecture 17 Pandas Operations 13:44
lecture 18 Data Input and Output 11:43
lecture 19 Pandas Exercises 2:21
lecture 20 Pandas Exercises - Solutions 7:10

Section 4 : Visualization Crash Course

lecture 21 Introduction to Python Visualization 1:9
lecture 22 Matplotlib Basics 8:55
lecture 23 Seaborn Basics 16:38
lecture 24 Data Visualization Exercises 2:40
lecture 25 Data Visualization Exercises - Solutions 7:30

Section 5 : Machine Learning Concepts Overview

lecture 26 About Certification Pdf
lecture 27 Supervised Learning Overview 8:15
lecture 28 Overfitting 7:53
lecture 29 Evaluating Performance - Classification Error 16:26
lecture 30 Evaluating Performance - Regression Error Met 5:30
lecture 31 Unsupervised Learning 4:38

Section 6 : Basic Artificial Neural Networks - ANNs

lecture 32 Introduction to ANN Section 2:9
lecture 33 Perceptron Model 10:34
lecture 34 Neural Networks 7:13
lecture 35 Activation Functions 10:32
lecture 36 Multi-Class Classification Considerations 10:27
lecture 37 Cost Functions and Gradient Descent 18:8
lecture 38 Backpropagation 14:42
lecture 39 TensorFlow vs. Keras Explained 2:7
lecture 40 Keras Syntax Basics - Part One - Preparing the 10:43
lecture 41 Keras Syntax Basics - Part Two - Creating and 13:54
lecture 42 Keras Syntax Basics - Part Three - Model Evalu 12:51
lecture 43 Keras Regression Code Along - Exploratory Data 18:45
lecture 44 Keras Regression Code Along - Exploratory Data 13:16
lecture 45 Keras Regression Code Along - Data Preprocessi 8:37
lecture 46 Keras Regression Code Along - Model Evaluation 11:17
lecture 47 Keras Classification Code Along - EDA and Prep 7:59
lecture 48 Keras Classification - Dealing with Overfittin 16:45
lecture 49 TensorFlow 2.0 Keras Project Options Overview 1:34
lecture 50 TensorFlow 2.0 Keras Project Notebook Overview 7:36
lecture 51 Keras Project Solutions - Exploratory Data Ana 20:27
lecture 52 Keras Project Solutions - Dealing with Missing 14:41
lecture 53 Keras Project Solutions - Dealing with Missing 11:57
lecture 54 Keras Project Solutions - Categorical Data 17:17
lecture 55 Keras Project Solutions - Data PreProcessing 3:39
lecture 56 Keras Project Solutions - Creating and Trainin 3:51
lecture 57 Keras Project Solutions - Model Evaluation 9:36
lecture 58 Tensorboard 18:16

Section 7 : Convolutional Neural Networks - CNNs

lecture 59 CNN Section Overview 1:25
lecture 60 Image Filters and Kernels 11:30
lecture 61 Convolutional Layers 13:55
lecture 62 Pooling Layers 6:41
lecture 63 MNIST Data Set Overview 4:34
lecture 64 CNN on MNIST - Part One - The Data 12:51
lecture 65 CNN on MNIST - Part Two - Creating and Traini 16:9
lecture 66 CNN on MNIST - Part Three - Model Evaluation 6:47
lecture 67 CNN on CIFAR-10 - Part One - The Data 11:18
lecture 68 CNN on CIFAR-10 - Part Two - Evaluating the Mo 7:0
lecture 69 Downloading Data Set for Real Image Lectures 5:18
lecture 70 CNN on Real Image Files - Part One - Reading 14:49
lecture 71 CNN on Real Image Files - Part Two - Data Proc 15:31
lecture 72 CNN on Real Image Files - Part Three - Creatin 13:31
lecture 73 CNN on Real Image Files - Part Four - Evaluat 8:43
lecture 74 CNN Exercise Overview
lecture 75 CNN Exercise Solutions 8:23

Section 8 : Recurrent Neural Networks - RNNs

lecture 76 RNN Section Overview 2:31
lecture 77 RNN Basic Theory 7:35
lecture 78 Vanishing Gradients 6:40
lecture 79 LSTMS and GRU 11:16
lecture 80 RNN Batches 7:44
lecture 81 RNN on a Sine Wave - The Data 8:22
lecture 82 RNN on a Sine Wave - Batch Generator 8:9
lecture 83 RNN on a Sine Wave - Creating the Model 15:15
lecture 84 RNN on a Sine Wave - LSTMs and Forecasting 13:19
lecture 85 RNN on a Time Series - Part One 9:44
lecture 86 RNN on a Time Series - Part Two 21:31
lecture 87 RNN Exercise 3:55
lecture 88 RNN Exercise - Solutions 21:42
lecture 89 Bonus - Multivariate Time Series - RNN and LST 16:3

Section 9 : Natural Language Processing

lecture 90 Introduction to NLP Section 5:52
lecture 91 NLP - Part One - The Data 4:26
lecture 92 NLP - Part Two - Text Processing 4:29
lecture 93 NLP - Part Three - Creating Batches 13:4
lecture 94 NLP - Part Four - Creating the Model 10:17
lecture 95 NLP - Part Five - Training the Model 9:42
lecture 96 NLP - Part Six - Generating Text 9:12

Section 10 : AutoEncoders

lecture 97 Introduction to Autoencoders 3:7
lecture 98 Autoencoder Basics 7:46
lecture 99 Autoencoder for Dimensionality Reduction
lecture 100 Autoencoder for Images - Part One 16:58
lecture 101 Autoencoder for Images - Part Two - Noise Rem 8:12
lecture 102 Autoencoder Exercise Overview 3:25
lecture 103 Autoencoder Exercise - Solutions 10:24

Section 11 : Generative Adversarial Networks

lecture 104 GANs Overview 8:47
lecture 105 Creating a GAN - Part One- The Data 4:30
lecture 106 Creating a GAN - Part Two - The Model 12:14
lecture 107 Creating a GAN - Part Three - Model Training.
lecture 108 DCGAN - Deep Convolutional Generative Adversa 6:26

Section 12 : Deployment

lecture 109 Introduction to Deployment 3:22
lecture 110 Creating the Model 16:47
lecture 111 Model Prediction Function 9:28
lecture 112 Running a Basic Flask Application 10:32
lecture 113 Flask Postman API 11:9
lecture 114 Flask API - Using Requests Programmatically 3:48
lecture 115 Flask Front End 19:36
lecture 116 Live Deployment to the Web 17:22