Curriculum For This Course

Introduction

1Lectures

Time 00:01:38

Framing Business Problems as Machine Learning Problems

3Lectures

Time 00:17:09

Technical Framing of ML Problems

8Lectures

Time 00:35:02

Introduction to Machine Learning

4Lectures

Time 00:14:22

Building Machine Learning Models

8Lectures

Time 00:35:11

Machine Learning Training Pipelines

3Lectures

Time 00:13:32

Machine Learning and Related Google Cloud Services

9Lectures

Time 00:42:55

Machine Learning Infrastructure and Security

5Lectures

Time 00:23:02

Exploratory Data Analysis and Feature Engineering

6Lectures

Time 00:56:00

Managing and Preparing Data for Machine Learning

4Lectures

Time 00:19:50

Building Machine Learning Models

5Lectures

Time 00:20:56

Training and Testing Machine Learning Models

4Lectures

Time 00:17:53

Machine Learning Serving and Monitoring

4Lectures

Time 00:12:45

Tuning and Optimizing Machine Learning Pipelines

2Lectures

Time 00:14:21

Tips and Resources

2Lectures

Time 00:09:15

Thank you for taking the course!

1Lectures

Time 00:00:33

Introduction
	1. Introduction	1:38

Framing Business Problems as Machine Learning Problems
	1. Defining ML Success Criteria	5:44
	2. Steps to Building ML Models	7:55
	3. Utilizing ML Models in Production	3:30

Technical Framing of ML Problems
	1. Supervised Learning - Classification	8:23
	2. Supervised Learning - Regression	3:25
	3. Unsupervised Learning	5:44
	4. Semi-supervised Learning	3:10
	5. Reinforcement Learning	2:51
	6. ML Model Input Structure	5:46
	7. ML Model Output Structure	1:57
	8. Risks to Successful ML Model Development	3:46

Introduction to Machine Learning
	1. 3 Categories of Machine Learning Problems	3:15
	2. 2 Approaches to Machine Learning	1:04
	3. Symbolic Machine Learning	5:43
	4. Neural Networks and Machine Learning	4:20

Building Machine Learning Models
	1. Features and Labels	2:28
	2. Feature Engineering	5:17
	3. Model Building	3:48
	4. Evaluating Models	4:48
	5. Gradient Descent and Backpropagation	7:22
	6. Troubleshooting Machine Learning Models	5:10
	7. Building Models in Google Cloud	3:40
	8. Using Pretrained Models	2:38

Machine Learning Training Pipelines
	1. Overview of ML Pipelines	6:11
	2. 3 Steps to Production	3:42
	3. Comprehensive ML Services	3:39

Machine Learning and Related Google Cloud Services
	1. Introduction to Vertex AI	3:04
	2. Vetex AI Datasets	5:53
	3. Vertex AI Featurestore	4:35
	4. Vertex AI Workbences	3:43
	5. Vetex AI Training	5:23
	6. Introduction to Cloud Storage	7:55
	7. Introduction to BigQuery	6:11
	8. Introduction to Cloud Dataflow	2:51
	9. Introduction to Cloud Dataproc	3:20

Machine Learning Infrastructure and Security
	1. Virtual Machines and Containers	6:11
	2. GPUs and TPUs	2:36
	3. Edge Devices	2:26
	4. Securing ML Models	5:30
	5. Protecting Privacy in ML Models	6:19

Exploratory Data Analysis and Feature Engineering
	1. Basic Statistics for Data Exploration	3:18
	2. Encoding Data	5:24
	3. Feature Selection	4:25
	4. Class Imbalance	6:15
	5. Feature Crosses	4:04
	6. TensorFlow Transforms	32:34

Managing and Preparing Data for Machine Learning
	1. Organizing and Optimizing Training Sets	4:39
	2. Handling Missing Data	5:59
	3. Handling Outliers in Data	6:00
	4. Avoiding Data Leakage	3:12

Building Machine Learning Models
	1. Choosing Models and Frameworks	4:34
	2. Interpretability of Models	4:32
	3. Transfer Learning	4:33
	4. Data Augmentation	4:13
	5. Troubleshooting Models	3:04

Training and Testing Machine Learning Models
	1. Training Data File Formats	6:08
	2. Hyperparameter Tuning	5:14
	3. Baselines and Unit Tests	4:05
	4. Distributed Training	2:26

Machine Learning Serving and Monitoring
	1. Google Cloud Serving Options	2:44
	2. Scaling Prediction Services	1:29
	3. Performance and Business Quality of Predictions	4:07
	4. Fairness in ML Models	4:25

Tuning and Optimizing Machine Learning Pipelines
	1. Optimizing Training Pipelines	9:36
	2. Optimizing Serving Pipelines	4:45

Tips and Resources
	1. Exam Strategies and Tips	6:45
	2. Additional Resources to Help Prepare for the Exam	2:30

Thank you for taking the course!
	1. Thank you for taking the course!	0:33

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