Section 1 : Introduction

Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 Course curriculum overview 8:3
Lecture 3 Knowledge requirements 3:29
Lecture 4 How to Approach this course Text
Lecture 5 Guide to Setting up your Computer Text
Lecture 6 Slides covered in this course Text
Lecture 7 Notes covered in this course Text
Lecture 8 About Certification Pdf

Section 2 : Machine Learning Pipeline - Research Environment

Lecture 9 Machine Learning Pipeline Overview 7:54
Lecture 10 Machine Learning Pipeline Feature Engineering 8:0
Lecture 11 Machine Learning Pipeline Feature Selection 10:4
Lecture 12 Machine Learning Pipeline Model Building 3:0
Lecture 13 Jupyter notebooks covered in this section Text
Lecture 14 Data Analysis - Demo 18:26
Lecture 15 Feature Engineering - Demo 12:31
Lecture 16 Feature Selection - Demo 3:51
Lecture 17 Model Building - Demo 4:31
Lecture 18 Getting Ready for Deployment - Demo 7:49
Lecture 19 Bonus Machine Learning Pipeline Additional Resources 2:11
Lecture 20 Randomness in Machine Learning - Setting the Seed Text
Lecture 21 Randomness in Machine Learning - Additional reading resources Text
Lecture 22 FAQ Where can I learn more about the pipeline steps Text

Section 3 : Machine Learning System Architecture

Lecture 23 Machine Learning System Architecture and Why it Matters 2:0
Lecture 24 Specific Challenges of Machine Learning Systems 5:57
Lecture 25 Machine Learning System Approaches 5:4
Lecture 26 Machine Learning System Component Breakdown 5:56
Lecture 27 Building a Reproducible Machine Learning Pipeline 11:17
Lecture 28 Additional Reading Resources Text

Section 4 : Building a Reproducible Machine Learning Pipeline

Lecture 29 Production Code overview 2:44
Lecture 30 Procedural Programming Pipeline 11:44
Lecture 31 Designing a Custom Pipeline 18:9
Lecture 32 Leveraging a Third Party Pipeline Scikit-Learn 8:34
Lecture 33 Third Party Pipeline Create Scikit-Learn compatible Feature Transformers 12:40
Lecture 34 Third Party Pipeline Closing Remarks 1:56
Lecture 35 Scikit-Learn Pipeline - Code Text
Lecture 36 Bonus Should feature selection be part of the pipeline 5:55
Lecture 37 Bonus Additional Resources on Scikit-Learn Text
Lecture 38 Bonus Resources to Improve as a Python Developer Text

Section 5 : Course Setup and Key Tools

Lecture 39 Section 5 1:55
Lecture 40 Section 5 3:37
Lecture 41 Section 5 3:46
Lecture 42 Section5 4:1
Lecture 43 Section5 1:52
Lecture 44 Section 5 1:33
Lecture 45 Section 5 2:38
Lecture 46 Section 5 0:42
Lecture 47 Section5 8:21
Lecture 48 Section5
Lecture 49 Section5 2:37
Lecture 50 Section 5
Lecture 51 Section 5 5:9
Lecture 52 Section 5 0:53

Section 6 : Creating a Machine Learning Pipeline Application

Lecture 53 6 2:0
Lecture 54 6 5:6
Lecture 55 6 4:9
Lecture 56 6 Text
Lecture 57 6
Lecture 58 6
Lecture 59 6 2:35
Lecture 60 6 7:27
Lecture 61 6 7:40
Lecture 62 6 1:55

Section 7 : Serving the model via REST API

Lecture 63 7
Lecture 64 7 4:35
Lecture 65 7 2:41
Lecture 66 7 4:10
Lecture 67 7 4:9
Lecture 68 7 2:5
Lecture 69 7 7:19
Lecture 70 7 1:2

Section 8 : Continuous Integration and Deployment Pipelines

Lecture 71 8 4:24
Lecture 72 8 1:26
Lecture 73 8 6:23
Lecture 74 8 7:59
Lecture 75 8 5:37
Lecture 76 8 0:54

Section 9 : Differential Testing

Lecture 77 9 2:15
Lecture 78 9 4:28
Lecture 79 9 3:1
Lecture 80 9 4:1
Lecture 81 9 1:41

Section 10 : Deploying to a PaaS (Heroku) without Containers

Lecture 82 10 4:4
Lecture 83 10 2:37
Lecture 84 10 4:59
Lecture 85 10 1:32
Lecture 86 10 3:41
Lecture 87 10 2:5

Section 11 : Running Apps with Containers (Docker)

Lecture 88 11 4:22
Lecture 89 11 2:48
Lecture 90 11 2:50
Lecture 91 11 3:33
Lecture 92 11 5:31
Lecture 93 11 1:17

Section 12 : Deploying to IaaS (AWS ECS)

Lecture 94 12 2:54
Lecture 95 12 2:37
Lecture 96 12 4:8
Lecture 97 12 3:30
Lecture 98 12 0:34
Lecture 99 12 3:24
Lecture 100 12 3:1
Lecture 101 12 2:57
Lecture 102 12 1:16
Lecture 103 12 5:23
Lecture 104 12 4:43
Lecture 105 12 7:49
Lecture 106 12 4:21
Lecture 107 12 0:53
Lecture 108 12 2:42
Lecture 109 12 1:34

Section 13 : A Deep Learning Model with Big Data

Lecture 110 Challenges of using Big Data in Machine Learning 2:8
Lecture 111 Introduction to a Large Dataset - Plant Seedlings Images 1:48
Lecture 112 Building a CNN in the Research Environment 9:55
Lecture 113 About Certification Pdf
Lecture 114 Reproducibility in Neural Networks 3:21
Lecture 115 Setting the Seed for Keras Text
Lecture 116 Seed for Neural Networks - Additional reading resources Text
Lecture 117 13 7:5
Lecture 118 13 4:0
Lecture 119 13 2:53

Section 14 : Common Issues found during deployment

Lecture 120 Troubleshooting Text