Section 1 : Introduction
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 Course curriculum overview 00:08:03 Duration
Lecture 3 Knowledge requirements 00:03:29 Duration
Lecture 4 How to Approach this course
Lecture 5 Guide to Setting up your Computer
Lecture 6 Slides covered in this course
Lecture 7 Notes covered in this course
Lecture 8 About Certification

Section 2 : Machine Learning Pipeline - Research Environment
Lecture 1 Machine Learning Pipeline Overview 00:07:54 Duration
Lecture 2 Machine Learning Pipeline Feature Engineering 00:08:00 Duration
Lecture 3 Machine Learning Pipeline Feature Selection 00:10:04 Duration
Lecture 4 Machine Learning Pipeline Model Building 00:03:00 Duration
Lecture 5 Jupyter notebooks covered in this section
Lecture 6 Data Analysis - Demo 00:18:26 Duration
Lecture 7 Feature Engineering - Demo 00:12:31 Duration
Lecture 8 Feature Selection - Demo 00:03:51 Duration
Lecture 9 Model Building - Demo 00:04:31 Duration
Lecture 10 Getting Ready for Deployment - Demo 00:07:49 Duration
Lecture 11 Bonus Machine Learning Pipeline Additional Resources 00:02:11 Duration
Lecture 12 Randomness in Machine Learning - Setting the Seed
Lecture 13 Randomness in Machine Learning - Additional reading resources
Lecture 14 FAQ Where can I learn more about the pipeline steps

Section 3 : Machine Learning System Architecture
Lecture 1 Machine Learning System Architecture and Why it Matters 00:02:00 Duration
Lecture 2 Specific Challenges of Machine Learning Systems 00:05:57 Duration
Lecture 3 Machine Learning System Approaches 00:05:04 Duration
Lecture 4 Machine Learning System Component Breakdown 00:05:56 Duration
Lecture 5 Building a Reproducible Machine Learning Pipeline 00:11:17 Duration
Lecture 6 Additional Reading Resources

Section 4 : Building a Reproducible Machine Learning Pipeline
Lecture 1 Production Code overview 00:02:44 Duration
Lecture 2 Procedural Programming Pipeline 00:11:44 Duration
Lecture 3 Designing a Custom Pipeline 00:18:09 Duration
Lecture 4 Leveraging a Third Party Pipeline Scikit-Learn 00:08:34 Duration
Lecture 5 Third Party Pipeline Create Scikit-Learn compatible Feature Transformers 00:12:40 Duration
Lecture 6 Third Party Pipeline Closing Remarks 00:01:56 Duration
Lecture 7 Scikit-Learn Pipeline - Code
Lecture 8 Bonus Should feature selection be part of the pipeline 00:05:55 Duration
Lecture 9 Bonus Additional Resources on Scikit-Learn
Lecture 10 Bonus Resources to Improve as a Python Developer

Section 5 : Course Setup and Key Tools
Lecture 1 Section 5 00:01:55 Duration
Lecture 2 Section 5 00:03:37 Duration
Lecture 3 Section 5 00:03:46 Duration
Lecture 4 Section5 00:04:01 Duration
Lecture 5 Section5 00:01:52 Duration
Lecture 6 Section 5 00:01:33 Duration
Lecture 7 Section 5 00:02:38 Duration
Lecture 8 Section 5 00:00:42 Duration
Lecture 9 Section5 00:08:21 Duration
Lecture 10 Section5
Lecture 11 Section5 00:02:37 Duration
Lecture 12 Section 5
Lecture 13 Section 5 00:05:09 Duration
Lecture 14 Section 5 00:00:53 Duration

Section 6 : Creating a Machine Learning Pipeline Application
Lecture 1 6 00:02:00 Duration
Lecture 2 6 00:05:06 Duration
Lecture 3 6 00:04:09 Duration
Lecture 4 6
Lecture 5 6
Lecture 6 6
Lecture 7 6 00:02:35 Duration
Lecture 8 6 00:07:27 Duration
Lecture 9 6 00:07:40 Duration
Lecture 10 6 00:01:55 Duration

Section 7 : Serving the model via REST API
Lecture 1 7
Lecture 2 7 00:04:35 Duration
Lecture 3 7 00:02:41 Duration
Lecture 4 7 00:04:10 Duration
Lecture 5 7 00:04:09 Duration
Lecture 6 7 00:02:05 Duration
Lecture 7 7 00:07:19 Duration
Lecture 8 7 00:01:02 Duration

Section 8 : Continuous Integration and Deployment Pipelines
Lecture 1 8 00:04:24 Duration
Lecture 2 8 00:01:26 Duration
Lecture 3 8 00:06:23 Duration
Lecture 4 8 00:07:59 Duration
Lecture 5 8 00:05:37 Duration
Lecture 6 8 00:00:54 Duration

Section 9 : Differential Testing
Lecture 1 9 00:02:15 Duration
Lecture 2 9 00:04:28 Duration
Lecture 3 9 00:03:01 Duration
Lecture 4 9 00:04:01 Duration
Lecture 5 9 00:01:41 Duration

Section 10 : Deploying to a PaaS (Heroku) without Containers
Lecture 1 10 00:04:04 Duration
Lecture 2 10 00:02:37 Duration
Lecture 3 10 00:04:59 Duration
Lecture 4 10 00:01:32 Duration
Lecture 5 10 00:03:41 Duration
Lecture 6 10 00:02:05 Duration

Section 11 : Running Apps with Containers (Docker)
Lecture 1 11 00:04:22 Duration
Lecture 2 11 00:02:48 Duration
Lecture 3 11 00:02:50 Duration
Lecture 4 11 00:03:33 Duration
Lecture 5 11 00:05:31 Duration
Lecture 6 11 00:01:17 Duration

Section 12 : Deploying to IaaS (AWS ECS)
Lecture 1 12 00:02:54 Duration
Lecture 2 12 00:02:37 Duration
Lecture 3 12 00:04:08 Duration
Lecture 4 12 00:03:30 Duration
Lecture 5 12 00:00:34 Duration
Lecture 6 12 00:03:24 Duration
Lecture 7 12 00:03:01 Duration
Lecture 8 12 00:02:57 Duration
Lecture 9 12 00:01:16 Duration
Lecture 10 12 00:05:23 Duration
Lecture 11 12 00:04:43 Duration
Lecture 12 12 00:07:49 Duration
Lecture 13 12 00:04:21 Duration
Lecture 14 12 00:00:53 Duration
Lecture 15 12 00:02:42 Duration
Lecture 16 12 00:01:34 Duration

Section 13 : A Deep Learning Model with Big Data
Lecture 1 Challenges of using Big Data in Machine Learning 00:02:08 Duration
Lecture 2 Introduction to a Large Dataset - Plant Seedlings Images 00:01:48 Duration
Lecture 3 Building a CNN in the Research Environment 00:09:55 Duration
Lecture 4 About Certification
Lecture 5 Reproducibility in Neural Networks 00:03:21 Duration
Lecture 6 Setting the Seed for Keras
Lecture 7 Seed for Neural Networks - Additional reading resources
Lecture 8 13 00:07:05 Duration
Lecture 9 13 00:04:00 Duration
Lecture 10 13 00:02:53 Duration

Section 14 : Common Issues found during deployment
Lecture 1 Troubleshooting