PDFs and exam guides are not so efficient, right? Prepare for your Google examination with our training course. The Professional Machine Learning Engineer course contains a complete batch of videos that will provide you with profound and thorough knowledge related to Google certification exam. Pass the Google Professional Machine Learning Engineer test with flying colors.
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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