[2019 머신러닝 스터디잼 중급반] Launching into Machine Learning - 목차

머신러닝 스터디잼 입문반을 들어봤는데 입문반은 API를 주고 머신러닝은 이런 식으로 활용된다를 실제로 조금 실습해보는 정도였어서 중급반도 이어 듣게 됐다. 입문반과는 달리 머신러닝을 처음 접하는 입장에서는 생소한 용어도 많고 더군다나 모든 코스가 영어로 진행되어 한 번에 이해하기 어려워서 공부한 걸 정리해보기로 했다.

 

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코세라 머신러닝 중급 강좌 : Launching into Machine Learing

• Introduction to Launching into ML

- Introduction
- Intro to Qwiklabs

 

 

• Practical ML

<Introduction to Practical ML>
- Introduuction
<Supervised Learning>
- Supervised Learning
- Regression and Classification
<ML History>
- Short History of ML : Linear Regression
- Short History of ML : Perception
- Short History of ML : Neural Networks
- Short History of ML : Decision Trees
- Short History of ML : Kernel Methods
- Short History of ML : Random Forests
- Short History of ML : Modern Neural Networks
- Modern Neural Networks(토론)

 

 

• Optimization

<Introduction to Optimization>
- Introduction
<Defining ML Models>
- Defining ML Models>
- Introducing the Natality Dataset
<Introducing Loss Functions>
- Introducing Loss Functions
<Gradient Descent>
- Gradient Descent
- Troubleshooting a Loss Curve
- ML Model Pitfalls
<TensorFlow Playground>
- Lab : Introducing the TensorFlow Playground
- Lab : TensorFlow Playground - Advanced
- Loss Curve Troubleshooting
<Performance Metrics>
- Performance Metrics
- Confusion Matrix

 

 

• Generalization and Samplig

<Introduction to Generalization and Sampling>
- Introduction
<Generalization>
- Generalization and ML Models
- When to Stop Model Training
<Sampling>
- Creating Repeatable Samples in BigQuery
<Demo of Splitting Datasets in BigQuery>
- Demo : Splitting Datasets in BigQuery
<Lab : Creating Repeatable Dataset Splits>
- Lab Introduction
- Lab : Creating Repeatable DatasetSplits (평가)
- Lab Solution Walkthrough
<Lab : Exploring and Creating ML Datasets>
- Lab Introduction
- Lab : Exploring and Creating ML Datasets (평가)
- Lab Solution Walkthrough

 

 

• Summary

- Module Summary

 

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출처 : https://sites.google.com/view/ml-studyjam2

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