Google Cloud Machine Learning Engineer Certification Prep

سرفصل های دوره

Building, Deploying, and Managing Machine Learning Services at Scale

1 - Introduction

1 - Introduction

2 - Working with Google Cloud

3 - How to Get Help When You are Stuck

2 - Framing Business Problems as Machine Learning Problems

1 - Quiz.html

4 - Identifying Business Problems that Benefit from ML

5 - Defining ML Success Criteria

6 - Steps to Building ML Models

7 - Utilizing ML Models in Production

3 - Technical Framing of ML Problems

2 - Quiz.html

8 - Supervised Learning Classification

9 - Supervised Learning Regression

10 - Unsupervised Learning

11 - Semisupervised Learning

12 - Reinforcement Learning

13 - ML Model Input Structure

14 - ML Model Output Structure

15 - Risks to Successful ML Model Development

4 - Machine Learning Training Pipelines

3 - Quiz.html

16 - Overview of ML Pipelines

17 - 3 Steps to Production

18 - Comprehensive ML Services

5 - Machine Learning and Related Google Cloud Services

4 - Quiz.html

19 - Introduction to Vertex AI

20 - Vetex AI Datasets

21 - Vertex AI Featurestore

22 - Vertex AI Workbences

23 - Vetex AI Training

24 - Introduction to Cloud Storage

25 - Introduction to BigQuery

26 - Introduction to Cloud Dataflow

27 - Introduction to Cloud Dataproc

6 - Machine Learning Infrastructure and Security

5 - Quiz.html

28 - Virtual Machines and Containers

29 - GPUs and TPUs

30 - Edge Devices

31 - Securing ML Models

32 - Protecting Privacy in ML Models

7 - Exploratory Data Analysis and Feature Engineering

6 - Quiz.html

33 - Basic Statistics for Data Exploration

34 - Encoding Data

35 - Feature Selection

36 - Class Imbalance

37 - Feature Crosses

38 - TensorFlow Transforms

8 - Managing and Preparing Data for Machine Learning

7 - Quiz.html

39 - Organizing and Optimizing Training Sets

40 - Handling Missing Data

41 - Handling Outliers in Data

42 - Avoiding Data Leakage

9 - Building Machine Learning Models

8 - Quiz.html

43 - Choosing Models and Frameworks

44 - Interpretability of Models

45 - Transfer Learning

46 - Data Augmentation

47 - Troubleshooting Models

10 - Training and Testing Machine Learning Models

9 - Quiz.html

48 - Training Data File Formats

49 - Hyperparameter Tuning

50 - Baselines and Unit Tests

51 - Distributed Training

11 - Machine Learning Serving and Monitoring

10 - Quiz.html

52 - Google Cloud Serving Options

53 - Scaling Prediction Services

54 - Performance and Business Quality of Predictions

55 - Fairness in ML Models

12 - Tuning and Optimizing Machine Learning Pipelines

11 - Quiz.html

56 - Optimizing Training Pipelines

57 - Optimizing Serving Pipelines

13 - Tips and Resources

58 - Exam Strategies and Tips

59 - Additional Resources to Help Prepare for the Exam

14 - Thank you for taking the course

60 - Thank you for taking the course

15 - Practice Test

1 - Machine Learning Engineer Practice Test.html

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تولید کننده: Udemy-Training