Curriculum For This Course
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Introduction
1 Lecture Time 00:01:38 -
Framing Business Problems as Machine Learning Problems
3 Lectures Time 00:17:09 -
Technical Framing of ML Problems
8 Lectures Time 00:35:02 -
Introduction to Machine Learning
4 Lectures Time 00:14:22 -
Building Machine Learning Models
5 Lectures Time 00:20:56 -
Machine Learning Training Pipelines
3 Lectures Time 00:13:32 -
Machine Learning and Related Google Cloud Services
9 Lectures Time 00:42:55 -
Machine Learning Infrastructure and Security
5 Lectures Time 00:23:02 -
Exploratory Data Analysis and Feature Engineering
6 Lectures Time 00:56:00 -
Managing and Preparing Data for Machine Learning
4 Lectures Time 00:19:50 -
Training and Testing Machine Learning Models
4 Lectures Time 00:17:53 -
Machine Learning Serving and Monitoring
4 Lectures Time 00:12:45 -
Tuning and Optimizing Machine Learning Pipelines
2 Lectures Time 00:14:21 -
Tips and Resources
2 Lectures Time 00:09:15 -
Thank you for taking the course!
1 Lecture Time 00:00:33
Video tutorials list
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Introduction
Video Name Time 1. Introduction 1:38 -
Framing Business Problems as Machine Learning Problems
Video Name Time 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
Video Name Time 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
Video Name Time 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
Video Name Time 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 9. Choosing Models and Frameworks 4:34 10. Interpretability of Models 4:32 11. Transfer Learning 4:33 12. Data Augmentation 4:13 13. Troubleshooting Models 3:04 -
Machine Learning Training Pipelines
Video Name Time 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
Video Name Time 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
Video Name Time 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
Video Name Time 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
Video Name Time 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 -
Training and Testing Machine Learning Models
Video Name Time 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
Video Name Time 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
Video Name Time 1. Optimizing Training Pipelines 9:36 2. Optimizing Serving Pipelines 4:45 -
Tips and Resources
Video Name Time 1. Exam Strategies and Tips 6:45 2. Additional Resources to Help Prepare for the Exam 2:30 -
Thank you for taking the course!
Video Name Time 1. Thank you for taking the course! 0:33
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