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You will develop the ability to define problems based on data and clearly explain the rationale and decision-making process behind them. Additionally, rather than focusing solely on the performance of a single model, you will acquire a pipeline-oriented mindset that evaluates the completeness and reliability of the entire machine learning workflow. Furthermore, you will strengthen your problem-solving skills by tracing back the causes of errors when they occur and deriving improvement directions, and through end-to-end project experience, you will acquire practical ML pipeline capabilities that can be immediately applied in real-world settings.

aisw

Machine Learning Pipeline

ai 코딩

AI 코딩

backend basics

백엔드이해하기

실습 중심

입문

초급

중급이상

Machine Learning(ML)

파이썬

Python

도커

Docker

텐서플로

Tensorflow

Pipeline Overview

Data Preparation

Model Development

Evaluation & Practice (t-Test)