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- AWS Certified Machine Learning - Specialty (MLS-C01)
AWS Certified Machine Learning - Specialty (MLS-C01) exam cover and Content
The course covers a wide range of topics that are relevant to the AWS Certified Machine Learning - Specialty exam, including data engineering, exploratory data analysis, modeling, and machine learning implementation and operations.
Some of the specific topics covered in the course include:
1. S3 data lakes: using Amazon S3 to store and process large amounts of data for machine learning
2. AWS Glue and Glue ETL: using AWS Glue to extract, transform, and load data for machine learning
3. Kinesis data streams, firehose, and video streams: using Amazon Kinesis to ingest and process streaming data for machine learning
4. DynamoDB: using Amazon DynamoDB to store and retrieve data for machine learning
5. Data Pipelines, AWS Batch, and Step Functions: using these tools to automate and orchestrate machine learning workflows
6. Using scikit-learn: using the scikit-learn library to build and evaluate machine learning models
7. Data science basics: understanding the principles of data science and machine learning
8. Athena and Quicksight: using Amazon Athena and Amazon QuickSight to query and visualize data for machine learning
9. Elastic MapReduce (EMR): using Amazon EMR to process and analyze large amounts of data for machine learning
10. Apache Spark and MLLib: using Apache Spark and MLLib to build and evaluate machine learning models
11. Feature engineering: understanding how to prepare data for machine learning by imputing missing values, identifying and handling outliers, binning data, applying transforms, encoding categorical data, and normalizing numerical data
12. Ground Truth: using Amazon SageMaker Ground Truth to label data for machine learning
13. Deep Learning basics: understanding the principles of deep learning and how to build deep learning models
14. Tuning neural networks and avoiding overfitting: understanding how to optimize the performance of neural networks and prevent overfitting
15. Amazon SageMaker: using Amazon SageMaker to build, train, and deploy machine learning models
16. Regularization techniques: understanding how to use regularization to prevent overfitting in machine learning models
17. Evaluating machine learning models: understanding how to evaluate the performance of machine learning models using metrics such as precision, recall, F1 score, and the confusion matrix
18. High-level ML services: using high-level machine learning services such as Amazon Comprehend, Amazon Translate, Amazon Polly, Amazon Transcribe, Amazon Lex, Amazon Rekognition, and more
19. Building recommender systems with Amazon Personalize: using Amazon Personalize to build recommendation systems
20. Monitoring industrial equipment with Lookout and Monitron: using Amazon Lookout for Equipment and Amazon Monitron to monitor and predict the performance of industrial equipment
21. Security best practices with machine learning on AWS: understanding how to secure machine learning solutions on the AWS platform.
Some of the specific topics covered in the course include:
1. S3 data lakes: using Amazon S3 to store and process large amounts of data for machine learning
2. AWS Glue and Glue ETL: using AWS Glue to extract, transform, and load data for machine learning
3. Kinesis data streams, firehose, and video streams: using Amazon Kinesis to ingest and process streaming data for machine learning
4. DynamoDB: using Amazon DynamoDB to store and retrieve data for machine learning
5. Data Pipelines, AWS Batch, and Step Functions: using these tools to automate and orchestrate machine learning workflows
6. Using scikit-learn: using the scikit-learn library to build and evaluate machine learning models
7. Data science basics: understanding the principles of data science and machine learning
8. Athena and Quicksight: using Amazon Athena and Amazon QuickSight to query and visualize data for machine learning
9. Elastic MapReduce (EMR): using Amazon EMR to process and analyze large amounts of data for machine learning
10. Apache Spark and MLLib: using Apache Spark and MLLib to build and evaluate machine learning models
11. Feature engineering: understanding how to prepare data for machine learning by imputing missing values, identifying and handling outliers, binning data, applying transforms, encoding categorical data, and normalizing numerical data
12. Ground Truth: using Amazon SageMaker Ground Truth to label data for machine learning
13. Deep Learning basics: understanding the principles of deep learning and how to build deep learning models
14. Tuning neural networks and avoiding overfitting: understanding how to optimize the performance of neural networks and prevent overfitting
15. Amazon SageMaker: using Amazon SageMaker to build, train, and deploy machine learning models
16. Regularization techniques: understanding how to use regularization to prevent overfitting in machine learning models
17. Evaluating machine learning models: understanding how to evaluate the performance of machine learning models using metrics such as precision, recall, F1 score, and the confusion matrix
18. High-level ML services: using high-level machine learning services such as Amazon Comprehend, Amazon Translate, Amazon Polly, Amazon Transcribe, Amazon Lex, Amazon Rekognition, and more
19. Building recommender systems with Amazon Personalize: using Amazon Personalize to build recommendation systems
20. Monitoring industrial equipment with Lookout and Monitron: using Amazon Lookout for Equipment and Amazon Monitron to monitor and predict the performance of industrial equipment
21. Security best practices with machine learning on AWS: understanding how to secure machine learning solutions on the AWS platform.