Exam AWS MLS Machine Learning

[MLS-C01] [Conclusion] Course Summary

Summary of the course

Posted by Oscaner on August 8, 2022

Lifecycle

Data Engineering

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

Gather your data
Handle missing data
Feature extraction
Decide which features are important
Encode categorical values
Numeric feature engineering
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

Modeling

Three types of models

Supervised machine learning
Unsupervised machine learning
Reinforcement machine learning

Algorithms

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

本文由 Oscaner 创作, 采用知识共享署名4.0 国际许可协议进行许可
本站文章除注明转载/出处外, 均为本站原创或翻译, 转载前请务必署名