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AWS Certified Machine Learning – Specialty (MLS-C01) Cert Prep: 3 Modeling

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Join MLOps expert and CTO Noah Gift to learn all about data engineering and prepare for the modeling portion of the AWS Certified Machine Learning – Specialty (MLS-C01) certification. Noah explains when to use machine learning, the difference between supervised and unsupervised learning, and the types of models that are available. He shows you how to train the model using the appropriate data. This process includes splitting the data into training and validation sets, choosing the right optimizer and loss function, and understanding the trade-offs between different model choices. After the model is trained, Noah guides you through how to evaluate it to ensure that it is performing well. This evaluation includes choosing the right metrics, understanding the confusion matrix, and performing cross-validation. Finally, Noah goes over how to interpret the model to understand what it is doing and how it can be improved.

This course was created by Noah Gift. We are pleased to host this training in our library.

  • -01 - Overview - -c01-cert-p
  • -02 - Determine when to use and when not to use ML
  • -03 - Know the difference between supervised and un
  • -04 - Select from among classification, regression
  • -05 - Select models - -c01-c
  • -06 - SageMaker Canvas demoalty-m
  • -07 - Train validation test split, cross-validation
  • -08 - Optimization - -c01-ce
  • -09 - Compute choice - -c01-
  • -10 - Neural network architecture
  • -11 - Avoid overfitting and underfitting
  • -12 - Select metrics - -c01-
  • -13 - Compare models using metrics
  • -14 - Conclusion - -c01-cert
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