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- Professional Machine Learning Engineer
Professional Machine Learning Engineer
Your Google Professional Machine Learning Engineer exam is just around the corner, right? So, it's high time to find an effective preparation tool! Our training course is what you really need! This is a series of videos led by the experienced IT instructors who will provide you with a detailed overview of the Professional Machine Learning Engineer certification test. Ace your Google Professional Machine Learning Engineer at the first attempt and obtain the Professional Machine Learning Engineer credential with ease.
97
69
5 h
5.0
Price $27.49
Today $24.99
Curriculum for Professional Machine Learning Engineer Video Course
1. Introduction
1 Lectures
00:01:38
2. Framing Business...
3 Lectures
00:17:09
3. Technical Framing of...
8 Lectures
00:35:02
4. Introduction to...
4 Lectures
00:14:22
5. Building Machine...
13 Lectures
00:20:56
6. Machine Learning...
3 Lectures
00:13:32
7. Machine Learning and...
9 Lectures
00:42:55
8. Machine Learning...
5 Lectures
00:23:02
9. Exploratory Data...
6 Lectures
00:56:00
10. Managing and...
4 Lectures
00:19:50
11. Training and Testing...
4 Lectures
00:17:53
12. Machine Learning...
4 Lectures
00:12:45
13. Tuning and...
2 Lectures
00:14:21
14. Tips and Resources
2 Lectures
00:09:15
15. Thank you for taking...
1 Lectures
00:00:33
Introduction
| Video Name | Time | |
|---|---|---|
| 1. Introduction | 1:38 |
| 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 |
| 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 |
| 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 |
| 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 |
| Video Name | Time | |
|---|---|---|
| 1. Overview of ML Pipelines | 6:11 | |
| 2. 3 Steps to Production | 3:42 | |
| 3. Comprehensive ML Services | 3:39 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Video Name | Time | |
|---|---|---|
| 1. Optimizing Training Pipelines | 9:36 | |
| 2. Optimizing Serving Pipelines | 4:45 |
| Video Name | Time | |
|---|---|---|
| 1. Exam Strategies and Tips | 6:45 | |
| 2. Additional Resources to Help Prepare for the Exam | 2:30 |
| Video Name | Time | |
|---|---|---|
| 1. Thank you for taking the course! | 0:33 |
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