[MLS-C01] [Conclusion] Course Summary

Summary of the course

Posted by Oscaner on August 8, 2022


Data Engineering

Seven steps to prepare you data for use in a machine learning model

  1. Gather your data
  2. Handle missing data
  3. Feature extraction
  4. Decide which features are important
  5. Encode categorical values
  6. Numeric feature engineering
  7. Split your data into training and testing datasets

Exploratory Data Analysis

  • Basic data analysis concepts and terminology
  • Kinesis Data Streams
  • Kinesis Data Firehose
  • Kinesis Video Streams
  • Kinesis Data Analytics
  • Visualize data for machine learning


Three types of models

  1. Supervised machine learning
  2. Unsupervised machine learning
  3. Reinforcement machine learning


Structured data

  • Linear Learner
  • Factorization Machines
  • XGBoost
  • K-Means
  • Principal Component Analysis
  • Random Cut Forest

Image data

  • Image Classification

Natural Language data

  • Sequence2Sequence
  • Neural Topic Modeling
  • Latent Dirichlet Allocation
  • Blazing Text

Time Series data

  • DeepAR

Implementation and Operation

  • To optimize the performance of your models you can use Automatic Model Tuning to find the hyperparameters that will give you the optimal performance
  • You can use SageMaker hosting to automatically scale your model to the performance needed for your model

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