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Curriculum for Professional Machine Learning Engineer Certification Video Course
Name of Video | Time |
---|---|
![]() 1. Introduction |
1:38 |
Name of Video | Time |
---|---|
![]() 1. Defining ML Success Criteria |
5:44 |
![]() 2. Steps to Building ML Models |
7:55 |
![]() 3. Utilizing ML Models in Production |
3:30 |
Name of Video | 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 |
Name of Video | 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 |
Name of Video | 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 |
Name of Video | Time |
---|---|
![]() 1. Overview of ML Pipelines |
6:11 |
![]() 2. 3 Steps to Production |
3:42 |
![]() 3. Comprehensive ML Services |
3:39 |
Name of Video | 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 |
Name of Video | 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 |
Name of Video | 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 |
Name of Video | 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 |
Name of Video | Time |
---|---|
![]() 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 |
Name of Video | 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 |
Name of Video | 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 |
Name of Video | Time |
---|---|
![]() 1. Optimizing Training Pipelines |
9:36 |
![]() 2. Optimizing Serving Pipelines |
4:45 |
Name of Video | Time |
---|---|
![]() 1. Exam Strategies and Tips |
6:45 |
![]() 2. Additional Resources to Help Prepare for the Exam |
2:30 |
Name of Video | Time |
---|---|
![]() 1. Thank you for taking the course! |
0:33 |
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Google Professional Machine Learning Engineer Training Course
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